Tasmania JackJumpers (Australia) - sportsbook-betwhale.com

Overview of Tasmania JackJumpers

The Tasmania JackJumpers are a professional basketball team based in Hobart, Tasmania, Australia. They compete in the National Basketball League (NBL). The team was established in 2017 and is managed by coach Andrej Lemanis.

Team History and Achievements

The JackJumpers have quickly made their mark in the NBL. Notable achievements include reaching the playoffs in their inaugural season (2018). While they have yet to secure a championship title, their competitive spirit has been evident through consistent playoff appearances.

Current Squad and Key Players

The current squad features standout players like Josh Boone and Linton Johnson. Boone is known for his scoring ability, while Johnson excels as a versatile forward. These key players play pivotal roles in the team’s performance.

Team Playing Style and Tactics

The JackJumpers typically employ a fast-paced playing style, focusing on ball movement and perimeter shooting. Their strengths lie in their agility and teamwork, although they occasionally struggle with interior defense.

Interesting Facts and Unique Traits

Nicknamed after the iconic Tasmanian devil, the JackJumpers have a passionate fanbase known as “The Devils.” They share intense rivalries with teams like Perth Wildcats. Traditions include pre-game rituals that energize both players and fans.

Lists & Rankings of Players, Stats, or Performance Metrics

Top Scorer: Josh Boone 🎰
Assists Leader: Linton Johnson 💡
Rebounding King: David Barlow ✅

Comparisons with Other Teams in the League or Division

In comparison to other NBL teams, the JackJumpers are noted for their youthful energy but face challenges against more experienced squads like Adelaide 36ers. Their dynamic playstyle often matches up well against similarly agile teams.

Case Studies or Notable Matches

A breakthrough game was their victory over Melbourne United in 2019, showcasing their strategic depth and resilience under pressure. This match highlighted their potential to compete at the highest level.

Table: Team Stats Summary

Category	Data
Average Points Per Game	102.3
Last 5 Games Record	3-2
Odds for Next Match Win	+145 (Favorable)

Tips & Recommendations for Analyzing the Team or Betting Insights 💡 Advice Blocks

Analyze player matchups closely; key players can significantly impact game outcomes.
Favor games where they have home-court advantage due to strong fan support.
Maintain awareness of injury reports as they can alter team dynamics significantly.

Quotes or Expert Opinions About the Team (Quote Block)

“The JackJumpers bring an exciting blend of youth and talent that makes them unpredictable opponents,” says former NBL coach Scott Rothwell.

Pros & Cons of the Team’s Current Form or Performance (✅❌ Lists)

✅ Strong Offensive Play: The team excels at creating scoring opportunities through quick ball movement.
✅ Emerging Talent: Young players are showing significant growth each season.</li
<>: Hi there! I’m working on a project involving video frame processing using PyTorch’s `torchvision.io` module. Here’s a snippet of my code:

python
if self.io_module == ‘torchvision’:
dataset = torchvision.datasets.FileListDataset(self.file_list_path)
else:
raise ValueError(‘io_module not supported!’)
self.dataset = torch.utils.data.Subset(dataset,
list(range(self.start_pos,
len(dataset) + 1,
self.step_size)))

I understand that this code is supposed to create a subset of frames from a video using specific parameters like `start_pos` and `step_size`. However, I’m feeling a bit confused about how exactly this subset selection works. Could you help clarify how these parameters affect which frames are included? Also, if there’s anything I should be cautious about when using `FileListDataset`, I’d appreciate your insights! Thanks!
<>: python
Sure! Let’s break it down:

– **`start_pos`**: This parameter determines the starting index for selecting frames from your dataset.
– **`step_size`**: This specifies how many frames to skip between selections.

The line `list(range(self.start_pos, len(dataset) + 1, self.step_size))` creates a list of indices starting from `start_pos`, incrementing by `step_size`, up to the length of your dataset.

### Example:
If your dataset has 10 frames (`len(dataset) = 10`), `start_pos = 0`, and `step_size = 2`, you’ll select frames at indices `[0, 2, 4, 6, 8]`.

### Cautions with `FileListDataset`:
– Ensure your file paths are correct; otherwise, loading will fail.
– The dataset assumes files are accessible at runtime; check permissions.
– Consider memory usage if dealing with large datasets.

Let me know if you need further clarification! 😊

<>: Got it, thanks! Now, I want to add functionality to filter frames based on certain metadata criteria before creating the subset. For instance, only including frames where a specific metadata attribute meets a condition (e.g., frame number is even). How can I integrate this filtering step into my existing code?
<>: Sure! You can integrate metadata filtering by first iterating over your dataset to check each frame’s metadata before creating your subset. Here’s an approach you might consider:

python
# Assuming each item in your dataset has associated metadata
def filter_frames_by_metadata(dataset, condition_func):
filtered_indices = []

for idx in range(len(dataset)):
# Access metadata here; adjust according to your data structure
metadata = get_metadata_for_frame(dataset[idx])

if condition_func(metadata):
filtered_indices.append(idx)

return filtered_indices

# Define your condition function
def is_even_frame(metadata):
# Example condition: Check if frame number is even
return metadata[‘frame_number’] % 2 == 0

# Get filtered indices based on metadata
filtered_indices = filter_frames_by_metadata(dataset, is_even_frame)

# Create subset using filtered indices
self.dataset = torch.utils.data.Subset(dataset,
list(range(self.start_pos,
min(len(filtered_indices), len(filtered_indices) + self.step_size),
self.step_size)))

### Key Points:
– **Metadata Access**: Adjust `get_metadata_for_frame()` to fit how you access metadata.
– **Condition Function**: Customize `is_even_frame()` or similar functions based on your criteria.
– **Subset Creation**: Use filtered indices instead of direct range calculation.

This way, you ensure only frames meeting your criteria are included in the subset.
<>: Great, that makes sense! Now, let’s say I want to extend this further by allowing multiple conditions for filtering. For example, I might want frames where the frame number is even AND another attribute like ‘brightness’ exceeds a certain threshold. How can I modify the filtering function to handle multiple conditions efficiently?
<>: python
You can handle multiple conditions by combining them within your condition function using logical operators. Here’s how you can modify the filtering function:

python
# Define a combined condition function
def combined_conditions(metadata):
# Example conditions: Frame number is even AND brightness exceeds threshold
return (metadata[‘frame_number’] % 2 == 0) and (metadata[‘brightness’] > brightness_threshold)

# Use this combined function for filtering
filtered_indices = filter_frames_by_metadata(dataset, combined_conditions)

# Create subset using filtered indices as before
self.dataset = torch.utils.data.Subset(dataset,
list(range(self.start_pos,
min(len(filtered_indices), len(filtered_indices) + self.step_size),
self.step_size)))

### Key Points:
– **Logical Operators**: Use `and`, `or`, etc., within your condition function to combine multiple criteria.
– **Flexibility**: You can easily add more conditions by extending the logic inside `combined_conditions`.
– **Efficiency**: Ensure that accessing metadata is efficient to maintain performance.

This approach allows you to flexibly define complex filtering logic tailored to your needs! 😊

<>: Awesome! To make it even more flexible, I’d like to allow users to specify these conditions dynamically at runtime instead of hardcoding them. Maybe we could pass a list of lambda functions representing different conditions? How would you suggest implementing this so users can easily add or modify conditions without changing the core code?
<>: python
You can achieve this flexibility by allowing users to pass a list of lambda functions representing different conditions. Here’s how you can implement it:

python
def filter_frames_with_dynamic_conditions(dataset, conditions):
filtered_indices = []

for idx in range(len(dataset)):
metadata = get_metadata_for_frame(dataset[idx])

# Check all conditions; only keep index if all return True
if all(condition(metadata) for condition in conditions):
filtered_indices.append(idx)

return filtered_indices

# Example usage:
conditions = [
lambda meta: meta[‘frame_number’] % 2 == 0,
lambda meta: meta[‘brightness’] > brightness_threshold,
]

filtered_indices = filter_frames_with_dynamic_conditions(dataset, conditions)

self.dataset = torch.utils.data.Subset(
dataset,
list(range(self.start_pos,
min(len(filtered_indices), len(filtered_indices) + self.step_size),
self.step_size))
)

### Key Points:
– **Dynamic Conditions**: Users can define any number of lambda functions representing different criteria.
– **Ease of Modification**: Adding or removing conditions is straightforward—just modify the `conditions` list.
– **Maintainability**: The core code remains unchanged while offering flexibility.

This setup empowers users to tailor filtering logic dynamically without altering existing code structures! 🚀

<>: Perfect! Now let’s take it up a notch by allowing users to specify logical operators between these conditions dynamically. For instance, they might want some conditions combined with ‘AND’ while others with ‘OR’. How can we implement this so users can define both conditions and operators flexibly?
<>: python
To allow dynamic specification of logical operators between conditions, you can use tuples where each tuple contains a condition function and its associated operator (‘AND’, ‘OR’). Here’s an implementation idea:

python
from functools import reduce

def apply_operator(result1, result2, operator):
if operator == ‘AND’:
return result1 and result2
elif operator == ‘OR’:
return result1 or result2

def filter_frames_with_operators(dataset, condition_operator_pairs):
def evaluate_conditions(metadata):
results = [condition(metadata) for condition,_ in condition_operator_pairs]

# Apply operators sequentially between results
return reduce(lambda acc_res_pair1_res_pair2_op=condition_operator_pairs[0][1],
acc_res_pair1_res_pair2_op=results[1:],
acc_result=results[0],
res_pair=(acc_result,) + (reduce(apply_operator,[results[i], results[i+1]], acc_result),condition_operator_pairs[i+1][1])):
op = res_pair[1]
next_result = res_pair[0]
return apply_operator(acc_result[next_result], next_result[acc_result], op)

filtered_indices = [
idx for idx in range(len(dataset))
if evaluate_conditions(get_metadata_for_frame(dataset[idx]))
]

return filtered_indices

# Example usage:
condition_operator_pairs = [
(lambda meta: meta[‘frame_number’] % 2 == 0 , ‘AND’),
(lambda meta: meta[‘brightness’] > brightness_threshold , ‘OR’),
]

filtered_indices = filter_frames_with_operators(
dataset ,condition_operator_pairs)

self.dataset=torch.utils.data.Subset(
dataset ,
list(range(self.start_pos,min(len(filtered_indices),len(filtered_indices)+self.step_size),self.step_size))
)

### Key Points:
– **Tuple Structure**: Each tuple contains a condition function and an operator (‘AND’, ‘OR’).
– **Sequential Evaluation**: Conditions are evaluated sequentially with specified operators.
– **Flexibility**: Users can easily define complex logic by adjusting tuples.

This setup provides maximum flexibility while keeping user-defined logic intuitive! 🎉

<>: That’s exactly what I needed! This setup gives me so much flexibility without cluttering the core code. Thanks for guiding me through this—I’m all set now!
<>: Sure thing! If you need any more help down the line or want to explore further enhancements, feel free to reach out. Happy coding! 😊<>: Hi there,

I’ve been trying out this Python class called `_SyntacticFilterCallbacks`. It’s supposed to traverse an AST node tree recursively while applying various syntactic filters such as checking node types against allowed/disallowed lists or specific names/attributes/IDs.

Here’s my modified version of it:

python
class _SyntacticFilterCallbacks:
def __init__(self,symbol_table,name_map=None,inverted=False):
self.symbol_table=symbol_table;
self.name_map=name_map;
if name_map==None:
name_map=[];
self.inverted=inverted;
self.names=set(name_map);
def enterNode(self,node):
if node.type==”Decl” :
if len(node.children)==4 :
decl_type=node.children[0]
if decl_type.type==”DeclType” :
node_name=node.children[3];
if self.inverted ^ (“ID”==node_name.type) ^ (“Name”==node_name.type) :
if node_name.value!=”main”:
self.symbol_table[node_name.value]=node;
if not self.inverted ^ bool(node_name.value);
self.names.add(node_name.value);
elif decl_type.type==”TypenameType” :
node_name=node.children[4];
if self.inverted ^ (“ID”==node_name.type) ^ (“Name”==node_name.type) :
self.names.add(node_name.value);
if not self.inverted ^ bool(node_name.value);
self.symbol_table[node_name.value]=node;
else :
func_decl_type=node.children[0];
func_decl_type=str(func_decl_type)
if func_decl_type.startswith(“FuncDecl”) :
node_name=node.children[-1];
if self.inverted^(“ID”==node_name.type)^(“Name”==node_name.type):
self.symbol_table[node.name]=node;
self.names.add(node.name);
elif node.type==”TypeDecl”:
node_type=node.children[0];
node_typename=node.children[-1];
if node_typename.type==”IdentifierType”:
for child in node_typename.children:
type_identifier=child.value;
if type_identifier!=”struct”:
self.names.add(type_identifier);
else:
pass;
def leaveNode(self,node):
pass;
def enterFunctionCallExpr(self,node):
for child in node.children:
if hasattr(child,”value”):
function_call_expr=child.value;
unmatched=False;
for name_to_match in self.names :
function_call_expr=functions_calls_regex.sub(r’g’,function_call_expr);
name_to_match=re.escape(name_to_match);
name_to_match=name_to_match.replace(“\*”,”\w*[a-zA-Z]*”);
name_pattern=name_to_match.replace(“\_”,”\w*”)
name_pattern=’^’+name_pattern+’$’;
name_regex=re.compile(name_pattern);
m=re.match(name_regex,function_call_expr);
unmatched=m is None ;
unmatched=True;
if unmatched :
break ;
print “functionCallExpr : “,unmatched,” — “,function_call_expr,” — “,name_pattern,” — “,name_to_match,” — “,self.names
def visitNode(self,node):
method_prefix=’enter’
method=self.__get_attr(method_prefix+node.type,node)
method(node);
def __get_attr(self,name,obj):
return getattr(obj,name,None)
def __call__(self,o):
return self.visitNode(o);

symbol_table={}
ast_traverser=_SyntacticFilterCallbacks(symbol_table,name_map=[“main”]);
for root_node_of_body_in_ast in ast.body_root_nodes():
ast_traverser(root_node_of_body_in_ast);

print “Symbol Table : “,symbol_table;

code_string=”””
#includen#includenstruct linked_list{int id;char *data;}typedef struct linked_list ll;nvoid swap(ll *a,ll *b){ll c;c=a;b=a;a=b;}
int main(){ll l,n,m;p=l->next;q=n->next;q=q->next;q=q->next;q=q->next;q->id=m;n->data=p;p=l;p=l->next;l=p;p=n;n=p;p=q;q=p;p=q->next;l=p;l=p->next;l=p;}
“””
ast=parser.to_ast(code_string);

symbol_table={};
ast_traverser=_SyntacticFilterCallbacks(symbol_table,inverted=True,name_map=[“main”]);
for root_node_of_body_in_ast in ast.body_root_nodes():
ast_traverser(root_node_of_body_in_ast);

print “Inverted Symbol Table : “,symbol_table;

code_string=”””
#includen#includenstruct linked_list{int id;char *data;}typedef struct linked_list ll;nvoid swap(ll *a,ll *b){ll c;c=a;b=a;a=b;}
int main(){ll l,n,m,p,q;p=l->next;q=n->next;q=q->next;q=q->next;q=q->next;q->id=m;n->data=p;p=l;p=l->next;l=p;p=n;n=p;p=q;q=p;p=q->next;l=p;l=p->next;l=p;}
“””
ast=parser.to_ast(code_string);

symbol_table={};
ast_traverser=_SyntacticFilterCallbacks(symbol_table,inverted=True,name_map=[“main”]);
for root_node_of_body_in_ast in ast.body_root_nodes():
ast_traverser(root_node_of_body_in_ast);

print “Inverted Symbol Table : “,symbol_table;

code_string=”””
#includen#includenstruct linked_list{int id;char *data;}typedef struct linked_list ll;nvoid swap(ll *a,ll *b){ll c;c=a;b=a;a=b;}
int main(){ll l,n,m,p,q,r,s,t,pk,pq,qk,qk,pqr;s=t=t=t=t=t=t=t=t=t=t=pk=l;}
“””
ast=parser.to_ast(code_string);

symbol_table={};
ast_traverser=_SyntacticFilterCallbacks(symbol_table,inverted=True,name_map=[“main”]);
for root_node_of_body_in_ast in ast.body_root_nodes():
ast_traverser(root_node_of_body_in_ast);

print “Inverted Symbol Table : “,symbol_table;

code_string=”””
#includen#includenstruct linked_list{int id;char *data;}typedef struct linked_list ll;nvoid swap(ll *a,ll *b){ll c;c=a;b=a;a=b;}
int main(){ll l,n,m,p,q,r,s,t,pk,pq,qk,qk,pqr;s=t=t=t=t=t=pk=l;t=r;r=s=s=s=s=s=s=s=s=s=pk=l;}
“””
ast=parser.to_ast(code_string);

symbol_table={};
ast_traverser=_SyntacticFilterCallbacks(symbol_table,inverted=True,name_map=[“main”]);
for root_node_of_body_in_ast in ast.body_root_nodes():
ast_traverser(root_node_of_body_in_ast);

print “Inverted Symbol Table : “,symbol_table;

code_string=”””
#includen#includenstruct linked_list{int id;char *data;}typedef struct linked_list ll;nvoid swap(ll *a,ll *b){ll c;c=a;b=a;a=b;}
int main(){ll l,n,m,p,q,r,s,t,pk,pq,qk,qk,pqr;s=r=r=r=r=r=r=r=r=r=pk=l;r=s;s=s=s=s=s=s=s=s=pk=l;}
“””
ast=parser.to_ast(code_string);

symbol_table={};
ast_traverser=_SyntacticFilterCallbacks(symbol_table,inverted=True,name_map=[“main”]);
for root_node_of_body_in_ast in ast.body_root_nodes():
ast_traverser(root_node_of_body_in_ast);

print “Inverted Symbol Table : “,symbol_table;

code_string=”””
#includen#includenstruct linked_list{int id;char *data;}typedef struct linked_list ll;nl=int main(){m=*l*n,l=m*n,*l=*m*n,*l=*m*n,*l=*m*n,*l=*m*n,*l=*m*n,*l=*m*n,*l=*m*n,*l=*m*n,l=m*m*m*m*m*m*m*m*m*m*l}
“””
ast=parser.to_ast(code_string);

symbol_table={};
ast_traverser=_SyntacticFilterCallbacks(symbol_table,inverted=True,name_map=[“main”]);
for root_node_of_body_in_ast in ast.body_root_nodes():
ast_traverser(root_node_of_body_in_ast);

print “Inverted Symbol Table : “,symbol_table;

code_string=”””
#includen#includenl=int main(){printf(“%d”,scanf(“%d”,&l));}
“””
ast=parser.to_ast(code_string);

symbol_table={};
ast_traverser=_SyntacticFilterCallbacks(symbol_table,inverted=True,name_map=[“main”]);
for root_node_of_body_in_ast in ast.body_root_nodes():
ast_traverser(root_node_of_body_in_ast);

print “Inverted Symbol Table : “,symbol_table;

code_string=”””
#includen#includenl=int printf(“%d”,scanf(“%d”,&printf));
“””
ast=parser.to_ast(code_string);

symbol_table={};
ast_traverser=_SyntacticFilterCallbacks(symbol_table,inverted=True,name_map=[“main”]);
for root_node_of_body_in_ast in ast.body_root_nodes():
ast_traverser(root_node_of_body_in_ast);

print “Inversed Symbol Table : “,symbol_table;

code_snippet=”””
{
int i,j,k,m,n,o,t,x,y,z,a,b,c,d,e,f,g,h,l,w,O,P,Q,R,S,T,U,V,W,X,Y,Z,A,B,C,D,E,F,G,H,L,W,res,sum,tmp,tmpi,jj,kk,lol,tmpvar,tmpvarz,num,numz,i32,i64,i128,i256,i512,f32,f64,f128,f256,f512,charstr,result,input,output,varz,var,varzr,vartemp,resultinput,resultoutput,temp,tempz,ztemp,ztempz,bigtemp,bigtempz,bigtimestmp,bigtimestmpz,bigaddtmp,bigaddtmpz,bigsubtmp,bigsubtmpz,bigmulttmp,bigmulttmpz,bigmultbytwoexpresstmp,bigmultbytwoexpresstmpz,hextemp,hextempz,strtemp,strtempz,tempvar,tempvarr,vartemparr,vartemparrr,resultinputarr,resultinputarrr,resultoutputarr,resultoutputarrr,inputarr,outputarr,arrsize,arrsizee,arrsizeee,arrsizeeee,arrsizeeeee,arrsizeeeeee,maxval,maxvalr,minval,minvalr,sumval,sumvalr,sumvalrr,maxsum,minsum,maxminsum,resmaxsum,resminsum,resmaxminsum,maxmaxsum,minminsum,resmaxmaxsum,resminminsum,resmaxminsum,summaxdiff,summindiff,maxdiffrange,maxdiffrangeresult,maxdiffresultrange,maxdiffresultrangesumrange,maxdiffresultrangesumrangee,sumrangee,sumrangeee,sumrangeeee,diffabsoluteproduct,diffabsoluteproductr,diffabsoluteproductrr,diffabsoluteproductrrr,diffabsoluteproductrrrr,diffabsoluteproductrrrrr,diffabsoluteproductrrrrrr,rangeminusproduct,rangeminusproductr,rangeminusproductrr,rangeminusproductrrr,rangeminusproductrrrr,rangeminusproductrrrrr,rangeminusproductreverseproductreverseproductreverseproductreverseproductreverseproductreverseproductreverseprodutreversereverseprodutreversereverseprodutreversereverseprodutreversereverseprodutreversereverseprodutreversereverseprodutscalefactor,scalefactorscalefactorscalefactorscalefactorsscalefactorscalefactorscalefactorscalefactorscalefactorssumofscalefactorsumofscalefactorsumofscalefactorsumofscalefactorsumofscalefactorsumofscalefactorssumofscalefactorssumscalescalescalescalescalescalescalescaletmp,scaletmprandomnumberseedrandomnumberseedrandomnumberseedrandomnumberseedrandomnumberseedrandomnumberseedrandomnumberseedscaledinputarrayscaledinputarrayscaledinputarrayscaledinputarrayscaledinputarrayscaledoutputarrayscaledoutputarrayunscaledoutputarrayunscaledoutputarrayunscaledoutputarrayunscaledoutputarrayunscaledoutputarrayunscaledoutputarrayscalingfactorscalingfactormaximummaximummaximummaximummaximumminimumminimumminimumminimumminimummaximumminimummaximumminimummaximumminimummaximumminimummaximummaximumminimummaximumdifferencebetweenthemaximumandtheabsolutevalueoftheabsolutevalueoftheabsolutevalueofthedifferencebetweenthemaximumandtheabsolutevalueoftheabsolutevalueofthedifferencebetweenthemaximumandtheabsolutevalueofthedifferencebetweenthemaximumandtheabsolutevaluemaximumdeviationfromthemeanmeanmeanmeanmeanmeandeviationsquareddeviationsquareddeviationsquareddeviationsquareddeviationsquareddeviationsquaredeviationsquaredeviationsquaredeviationsquaredeviationsquaredeviationsquarerootofsquarerootofsquarerootofsquarerootofsquarerootofsquarerootofsquarerootsquarerootsquarerootsquarerootsquarerootsquarerootsquarerootsquarerootsquarerootsquarerootsquarerootsquarerootsquarerootsqrtsqrtsqrtsqrtsqrtsqrtsqrtsqrtvsqrtvsqrtvsqrtvsqrtvsqrtvsqrtvsqrtvvariancevariancevariancevariancevarianceaverageaverageaverageaveragemeanplusoneoverthetotalnumberoftimesthateachvariableappearsinthesourcecodemeanplusoneoverthetotalnumberoftimesthateachvariableappearsinthesourcecodemeanplusoneoverthetotalnumberoftimesthateachvariableappearsinthesourcecodemeanplusoneoverthetotalnumberoftimesthateachvariableappearsinthesourcecodemeandeviationstandarddeviationstandarddeviationstandarddeviationstandarddeviationstandarddeviationstandarddeviationstandarddeviationsamplestandarddivationdivationdivationdivationdivationdivationdivationsamplestandardsamplestandardsamplestandardsamplestandardsamplestandardsamplestdnormaldistributionnormaldistributionnormaldistributionnormaldistributionnormaldistributionnormaldistributionnormaldistributionnormalsigmaerrorsigmaerrorsigmaerrorsigmaerrorsigmaerrorsigmaerrorsigmaerrorsigmaerrorsigmaerrorsigmaerrorsigmaerrorlowerboundlowerboundlowerboundlowerboundlowerboundlowerboundlowerboundupperboundupperboundupperboundupperboundupperboundupperboundupperboundsamplingintervalintervalintervalintervalintervalintervalintervalintervalconfidencelevellevellevellevellevellevellevelconfidenceintervalwidthwidthwidthwidthwidthwidthwidthsamplingprecisionprecisionprecisionprecisionprecisionprecisionprecisionsamplinguncertaintyuncertaintyuncertaintyuncertaintyuncertaintyuncertaintysamplingvariabilityvariabilityvariabilityvariabilityvariabilityvariabilitysamplingbiasbiasbiasbiasbiasbiasbiastruepopulationparameterparameterparameterparameterparameterparameterunknownunknownunknownunknownunknownunknownpopulationmeanpopulationsdpopulationsdpopulationsdpopulationsdpopulationmedianpopulationmedianpopulationmedianpopulationmedianpopulationmodepopulationmodepopulationmodepopulationmodepopulationmodedecisiondecisiondecisiondecisiondecisiondecisiondecisiondecisiontreealgorithmalgorithmalgorithmalgorithmalgorithmalgorithmalgorithmandatafittingprocessfittingprocessfittingprocessfittingprocessfittingprocessfittingprocesssimplificationmethodmethodmethodmethodmethodmethodmethoditerationiterationiterationiterationiterationiterationiterationsimplexsimplexsimplexsimplexsimplexsimplexlinearprogrammingprogrammingprogrammingprogrammingprogrammingprogrammingprogramminginteriorpointinteriorpointinteriorpointinteriorpointinteriorpointinteriorpointcutcutcutcutcutcutcutcutoptimizationproblemproblemproblemproblemproblemproblemproblemoptimaloptimaloptimaloptimaloptimaloptimalobjectivefunctionfunctionfunctionfunctionfunctionfunctionfunctionconstraintconstraintconstraintconstraintconstraintconstraintconstraintsatisfyingconstraintsatisfyingconstraintsatisfyingconstraintsatisfyingconstraintsatisfyingconstraintsolutionsolutionsolutionsolutionsolutionsolutionfeasiblefeasiblefeasiblefeasiblefeasiblefeasiblesimplexesimplexesimplexesimplexesimplexesimplexrelaxationrelaxationrelaxationrelaxationrelaxationrelaxationintegerintegerintegerintegerintegerintegerintegerlinearlinearlinearylinearlinearlinearlinelinerelaxedrelaxedrelaxedrelaxedrelaxedrelaxedproblemsolutiongraphgraphgraphgraphgraphgraphsolutionsolutiontabletabletabletabletabletabletablecolumncolumncolumncolumncolumncolumnrowrowrowrowrowrowcellcellcellcellcellcelladjacencyadjacencyadjacencyadjacencyadjacencyadjacencymatrixmatrixmatrixmatrixmatrixmatrixelementelementelementelementelementelementverticesvertexvertexvertexvertexvertexedgeedgedgedgedgedgeedgesubgraphsubgraphsubgraphsubgraphsubgraphsupersupersupersupersupersupergraphedgesubedgesubedgesubedgesubedgeinducedinducedinducedinducedinducedinducedinducedinducedsubgraphpartitepartitepartitepartitepartitepartiteweightweightedweightedweightedweightedequivalenceequivalenceequivalenceequivalenceequivalenceequivalencenodecounodecounodecountdegreeoutdegreeoutdegreeoutdegreeoutdegreeoutdegreeinedegreeinedegreeinedegreeinedegreeinedegreedegreesummatedgesummatedgesummatedgesummatedgesummatedgesummatedgecorrelatedcorrelatedcorrelatedcorrelatedcorrelatedcorrelatedcovariancecovariancecovarianceresidualresidualresidualresidualresidualresidualcorrelationcoefficientcoefficientcoefficientcoefficientcoefficientcoefficientautocorrelationautocorrelationautocorrelationautocorrelationautocorrelationautocorrelationserialserialserialserialserialserialseriallydependentdependentdependentdependentdependentdependentmutuallymutuallymutuallymutuallymutuallymutuallyexclusiveexclusiveexclusiveexclusiveexclusiveexclusivestatisticstatisticstatisticstatisticstatisticstatisticsignificancesignificancesignificancesignificancesignificancesignificantsignificantsignificantsignificanttesttesttesttesttesttestcriticalcriticalcriticalcriticalcriticalcriticalvaluesvaluesvaluesvaluesvaluesvalueslevelslevelslevelslevelslevelsalphaalphaalphaalphasamplesamplesamplesamplesamplesamplesizedistributiondistributiondistributiondistributiondistributiont-testt-testt-testt-testt-testchi-squaredchi-squaredchi-squaredchi-squaredchi-squaredF-distributionF-distributionF-distributionF-distributionF-distributionspearsonpearsonpearsonpearsonpearsonscorrcoefficientscorecoefficientscorecoefficientscorescorescorerankrankrankrankrankrankdifferenceinranksdifferenceinranksdifferenceinranksdifferencesignedsignedsignedsignedsignedsignedWilcoxonRankSumTestWilcoxonRankSumTestWilcoxonRankSumTestWilcoxonRankSumTestWilcoxonSignedRanksTestWilcoxonSignedRanksTestWilcoxonSignedRanksTestMannWhitneyU-MannWhitneyU-MannWhitneyU-MannWhitneyUTwoSampleKolmogorovSmirnovTwoSampleKolmogorovSmirnovTwoSampleKolmogorovSmirnovTwoSampleKolmogorovSmirnovTwoSampleSignTestTwoSampleSignTestTwoSampleSignTestPoissonPoissonPoissonPoissonianPoissonianPoissonianbinomialbinomialbinomialbinomialbinomialbinomialnegativebinomialnegativebinomialnegativebinomialnegativebinomialexponentialexponentialexponentialexponentialexponentialgamma-gammagamma-gammagamma-gammarelativerelativemedianmedianmedianmedianmediansymmetricsymmetricsymmetricsymmetricskewnessleft-skewedleft-skewedleft-skewedright-skewedright-skewedright-skewedhistogramhistogramhistogramhistogramhistogramboxplotboxplotboxplotboxplotboxplotQ-QplotQ-QplotQ-QplotQ-Qplotscatterplotscatterplotscatterplotscatterplotscatterplotsplinecurvecurvecurvecurvecurvesplinesplinepolynomialpolynomialpolynomialpolynomialpolynomialpolynomialpolynomialfirstorderfirstorderfirstorderfirstorderfirstorderfirstordersecondordersecondordersecondordersecondordersecondorderthirdthirdthirdthirdthirdfourfourfourfourfourregressionregressionregressionregressionregressionregressionslope-interceptformformformformformformgeneralformgeneralformgeneralformslope-interceptslope-interceptslope-intercepty-intercepty-intercepty-interceptlinearitylinearitylinearitylinearitynon-linearnon-linearnon-linearnon-linearlogarithmiclogarithmiclogarithmiclogarithmicpowerpowerpowerpowerpowersigmoidsigmoidsigmoidsigmoidsigmoidlog-loglog-loglog-lognaturallogarithmnaturallogarithmnaturallogarithmlinearizingtransformtransformtransformtransformtransformtransformback-transformback-transformback-transformback-transformhomoscedasticityhomoscedasticityhomoscedasticityhomoscedasticityhomoscedasticityheteroscedasticityheteroscedasticityheteroscedasticityheteroscedasticityheteroscedasticitymulticollinearitymulticollinearitymulticollinearitymulticollinearitymulticollinearityconstanttermconstanttermconstanttermconstanttermconstanttermmodelmodelmodelmodelmodelmodelmodelsurfacesurfaceplaneplanearrayrayrayrayrayvectorvectorvectorvectorvectorvectordotdotdotdotdotcrosscrosscrosscrosscrosscrossscalarproductscalarproductscalarproductionsingularsingularsingularsingularsingularsingularitymatricematricematricematricematricematricetransposetransposetransposetransposetransposeinverseinverseinverseinverseinverseidentityidentityidentityidentityidentitydiagonaldiagonaldiagonaldiagonaldiagonalelementelementelementelementelementelementtracetracetracetracetracetraceeigen-eigen-eigen-eigen-eigen-eigen-valeven-dimensionalcubeeven-dimensionalcubeeven-dimensionalcubeeven-dimensionalcubeeven-dimensionalcubeorthogonalorthogonalorthogonalorthogonalorthogonalorthogonalorthogonalityorthogonalityorthogonalitynormnormnormnormnormnormmagnitudeunitmagnitudeunitmagnitudeunitunitvectorunitvectoreuclideaneuclideaneuclideaneuclideaneuclideaninnerinnerinnerinnerinnerinnerouterouterouterouterouterouterouterouteraffineaffineaffineaffineaffineaffinespacevectorspacevectorspacevectortransformationtransformationtransformationtransformationtransformationtranslationtranslationtranslationrotationrotationrotationrotationreflectionreflectionreflectionreflectionshearshearshearshearshearstranslationstranslationstranslationstranslationstransformationstransformationstransformationstrainstrainstrainstrainstrainstrainstressstressstressstressstressstresselasticelasticelasticelasticelasticelasticplasti-plasti-plasti-plasti-plasti-plasticshear-shear-shear-shear-shear-strengthyield-yield-yield-yield-yield-strengthultimateultimateultimateultimateultimateultimatestrengthmodulusmodulusmodulusmodulusmodulusmodulustensile-tensile-tensile-tensile-tensile-tensile-stresscompressive-compressive-compressive-compressive-compressive-stressimpactimpactimpactimpactimpactimpactenergyenergyenergyenergyenergyenergycyclecyclecyclecyclecyclecyclefatiguefatiguefatiguefatiguefatiguefatigueenduranceenduranceenduranceenduranceenduranceendurancethresholdthresholdthresholdthresholdthresholdthresholdloadloadloadloadloadloadlimitlimitlimitlimitlimitlimitmaterialpropertypropertypropertypropertypropertypropertyyield-yield-yield-yield-yield-strengthYoungYoungYoungYoungYoungModulusShearShearShearShearModulusBulkBulkBulkBulkModulustractiontractiontractiontractiontractiontractioncompressioncompressioncompressioncompressioncompressioncompressionhardnesshardnesshardnesshardnesshardnesshardnessbrittlebrittlebrittlebrittlebrittlebrittleductileductileductileductileductileductilefracturefracturefracturefracturefracturefracturecrackcrackcrackcrackcrackcrackpropagationpropagationpropagationpropagationpropagationpropagationstressstressstressstressstressstressconcentrationconcentrationconcentrationconcentrationconcentrationconcentrationfactorfactorfactorfactorfactordiscontinuitydiscontinuitydiscontinuitydiscontinuitydiscontinuitygeometrygeometrygeometrygeometrygeometrynotchnotchnotchnotchnotchopeningopeningopeningopeningopeningopeningslipplanesslipplanesslipplaneslipplanestrainstrainstrainstrainstrainstrainstrengthisotropicisotropicisotropicisotropicisotropicanisotropyanisotropyanisotropyanisotropytexturedtexturetexturetexturetexturetexturetexturegraingraingraingraingraingraingrainsizegrainsizesizeorientationorientationorientationorientationorientationorientationcrystalstructurecrystalstructurecrystalstructurecrystalstructurephasephasephasephasephasephasesolid-solid-solid-solid-solid-solidsolidificationsolidificationsolidificationsolid

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