Tensile Strength Analysis of Bolt Suppliers

Your firm works in construction and buys ASTM A325M8S bolts in very large numbers.

You are interested in purchasing bolts from different suppliers and have obtained and tested samples of 200 bolts from three suppliers: Allnutt, Boltzman, Coachers.

You have tested these in your new bolt testing machine, purchased after you had discovered your old bolt testing machine was performing erratically. (For the purposes of this question, you are safe to assume that the new machine works perfectly and that its results are in MPa.)

Your firm wants to ensure that the tensile strengths of the bolts it buys are

• as high as possible
• as consistent as possible (i.e., the variability of bolt strengths is as lowas possible)

It is critical that the tensile strength of these bolts exceeds 830MPa.

You should draw on the unit contentconcerning summary statistics to answer this question.

On the basis of the measurements recorded in Bolts.csv, which supplier (if any) would you recommend, and why?
(Provide any code or visualisations you use to justify your response.)

Edit your response to this question here.

filename = ‘Bolts.xls’;

[numbers, TEXT, file] = xlsread(filename);

T = cell2mat(st1)

t1=T(1:100)

t2=T(101:200)

t3=T(201:400)

for count1

limit=850;

for i=1:200

    if (t1(i,1))>=limit

    {

     count1=count1+1    

     else

     {

         count1+1

      }

end

     }

    end

for count2

for( i=1:200)

    if (t2(i,1))>=limit

{

     count2=count2+1   

}

     else

{

         count2=count2

}

     end

end

for count3

for( i=1:200)

    if (t3(i,1))>=limit

{

     count3=count3+1

}    

     else

{

         count3=count3

}

     end

        printf(‘Tensile strength  count >830 from Allnuttn’,count1);

        printf(‘Tensile strength  count >830 from Boltzmannn’,count2);

        printf(‘Tensile strength  count > 830 from Coachersn’,count3);

histfit(t1)

true

histfit(t2)

true

histfit(t3)

false

Tensile strengths  count more than 830 from Allnutt= 40.

Tensile strengths  count more than 830 from Boltzmann= 100.

Tensile strengths  count more than 830 from Coachers= 160(approx).

ILLUSTRATION 1

From statistical analysis it has been found that tensile strength of the Bolt supplied by Coacher is having the highest count compared to other two suppliers. And from the bar plot, we can conclude that consistency of bolt strength or the variability of strength from coacher is lower compared to other two sellers

The state government’s Department of the Environment and Heritage Protection are concerned about air pollution levels in Brisbane city. You have been provided with data for two sites, the Brisbane CBD and South Brisbane, for 2010 and 2016 in the following four files:

• brisbanecbd-aq-2010.csv
• brisbanecbd-aq-2016.csv
• southbrisbane-aq-2010.csv
• southbrisbane-aq-2016csv

You have been asked to advise the Government:

• If air pollution is related across the two sites, and if so, has this relationship changed between 2010 and 2016

Has the level of air pollution at either site changed from 2010 to 2016? Have these changes across the two sites been consistent?

You have been advised to focus on the PM10 data series.

You should draw on the unit contentconcerning summary statistics and correlation to answer this question.

(Provide any code or visualization’s you use to justify your response.)

Edit your response to this question here

filename = ‘brisbanecbd-aq-2010.xls’;

sheet = 1;

[numbers, TEXT, file] = xlsread(filename);

br1=file(:,7);

S1 = cell2mat(br1);

filename = ‘brisbanecbd-aq-2016.xls’;

sheet = 1;

[numbers, TEXT, file] = xlsread(filename);

br2=file(:,7);

S2 = cell2mat(br2);

filename = ‘southbrisbane-aq-2010.xls’;

sheet = 1;

[numbers, TEXT, file] = xlsread(filename);

PM10 Air Pollution Levels in Brisbane

br3=file(:,7);

S3 = cell2mat(br3);

filename = ‘southbrisbane-aq-2016.xls’;

sheet = 1;

[numbers, TEXT, file] = xlsread(filename);

br4=file(:,7);

S4 = cell2mat(br4); //concatenating the cells in the matrix

size(S1);

size(S2);

size(S3);

size(S4);

z=1:1:8760;

S1=S1(1:8760);

S2=S2(1:8760);

S3=S3(1:8760);

S4=S4(1:8760);

[S1(1:8760) S2(1:8760) S3(1:8760) S4(1:8760)];

hist(S1)//histogram(S1)

hold on

hist(S2)//histogram(S2)

Your engine testing company has been evaluating the relationship between engine capacity (in cubic cm) and particulate emissions (in particles per milliliter) across a range of engines. Now you have received results from tests of 100 ethanol-fuelled engines and your supervisor has sent you the following email about that:

Take a look at the emissions data for the 100 ethanol engines in thefile “Engines.csv” and write an email to your supervisor explaining your findings.You should draw on the unit content concerning correlation and regression to answer this question.
(Provide any code or visualisations you use to justify your response.)

Edit your response to this question here

filename = ‘Engine.xls’;

sheet = 1;

[numbers, TEXT, file] = xlsread(filename);

eng1=file(:,2);

eng2=file(:,3);

S1 = cell2mat(eng1);   //concatenating cells with the matrix

S2 = cell2mat(eng2);

[S1 S2] //scattering of the cells after the desired concatenation

scatter(S1,S2)

R = corcoeff(S1,S2) //calculating the corelation coefficient

corr(S1,S2)

fitlm(S1,S2) //adjusting the size to fit

Estimated Coefficients:

                   Estimate        SE        tStatpValue

                   _________    _________    ______    __________

    (Intercept)       90.767        4.237    21.423    9.3819e-39

    x1             -0.014376    0.0014379    -9.998    1.2227e-16

Number of observations: 100, Error degrees of freedom: 98

Root Mean Squared Error: 3.61

R-squared: 0.505,  Adjusted R-Squared 0.5

F-statistic vs. constant model: 100, p-value = 1.22e-16

You work for a construction firm who need to be able to accurately predict the compressive strength of concrete given variables including the concrete composition and its age. You are aware that the relationship between these different components and the concrete strength is complex, however you have been asked to investigate how well a simple linear regression model works for prediction. Using the provided data (Concrete_Data.xls), develop models to predict:

• Concrete strength from the single best indicator variable;
• Concrete strength from all variables.

With the second model, determine if any variables are not contributing significantly to the model, and what impact removing these has on prediction performance. Comment on the final model and its accuracy.

You should draw on the unit contentconcerning correlation and regression to answer this question.

(Provide any code or visualisations you use to justify your response.)

Edit your response to this question here

compressive strength, concrete composition and its age

filename = ‘Concrete_Data.xls’;

sheet = 1;

[numbers, TEXT, file] = xlsread(filename);

no1=file(1);

no2=file(2);

no3=file(3);

no4=file(4);

no5=file(5);

no6=file(6);

no7=file(7);

no8=file(8);

no9=file(9);

Timestamp1 = cell2mat(no1);

Timestamp2 = cell2mat(no2);

Timestamp3 = cell2mat(no3);

Timestamp4 = cell2mat(no4);

Timestamp5 = cell2mat(no5);

Timestamp6 = cell2mat(no6);

Timestamp7 = cell2mat(no7);

Timestamp8 = cell2mat(no8);

Timestamp9 = cell2mat(no9);

plot(T1,T9,’*’)

lsline

X=[ones(size(T9)) T9]; //adjusting the size from T1 To T9 beta=regress(T1,X)

% scatter(T1,yCalc1) //calculating the %age of scattering

solution:  

y = 0.086x + 13.445
R² = 0.2508

CORRL: 0.497832952

From statistical analysis it has been found that Cement (component 1)(kg in a m^3 mixture) having the highest impact on Concrete compressive strength(MPa, megapascals). It is holding positive linear correlation and the correlation coefficient is found to be 0.497832722. Blast Furnace Slag (component 2)(kg in a m^3 mixture) and Fly Ash (component 3)(kg in a m^3 mixture) has low effect on Concrete compressive strength(MPa, megapascals). Superplasticizer (component 5)(kg in a m^3 mixture has positive impact on  Concrete compressive strength(MPa, megapascals)

Brisbane City Council (BCC) is considering upgrades to the bikeway networks. They are using data they have gathered from sensors placed along the bike paths, which record the number of cyclists, to plan the upgrades, however there have been a number of sensor failures which have resulted in their dataset missing a number of entries. BCC have requested that you investigate if it possible to predict missing data from data gathered from other sensors on the bike path network.

You have been provided with three years worth of data (Bike-Ped-Auto-Counts-2014.csv, Bike-Ped-Auto-Counts-2015.csv, and Bike-Ped-Auto-Counts-2016.csv). As an initial investigation, you have been asked to consider only these data series:

• BicentennialBikewayCyclistsInbound
• GoBetweenBridgeCyclistsInbound
• KangarooPointBikewayCyclistsInbound
• NormanParkCyclistsInbound
• RiverwalkCyclistsInbound
• StoryBridgeWestCyclistsInbound

Using the three years data, you are to:

• Determine which counters are best suited to predicting the missing data in others (i.e. which, if any counters, could be used to predict BicentennialBikewayCyclistsInbound).
• Predict missing data where appropriate to generate a more “complete” database.
• Comment on the resulting corrected dataset. In particular:
  • What problems, if any, may arise from this approach?
  • How effective has this been in reducing missing data?
  • How trustworthy are the predicted values?

You should draw on the unit contentconcerning correlation and regression to answer this question.

(Provide any code or visualisations you use to justify your response.)

Edit your response to this question here

filename =’bike_ques.xls’;

sheet = 1;

[numbers, TEXT, file] = xlsread(filename);

no1=file(1);

no2=file(2);

no3=file(3);

no4=file(4);

no5=file(5);

no6=file(6);

no7=file(7);

no8=file(8);

no9=file(9);

Timestamp1 = cell2mat(no1);

Timestamp2 = cell2mat(no2);

Timestamp3 = cell2mat(no3);

Timestamp4 = cell2mat(no4);

Timestamp5 = cell2mat(no5);

Timestamp6 = cell2mat(no6);

Timestamp7 = cell2mat(no7);

Timestamp8 = cell2mat(no8);

Timestamp9 = cell2mat(no9);

plot(T1,T9,’*’)

lsline

X=[ones(size(T9)) T9]; //adjusting the size from T1 To T9

beta=regress(T1,X)

% scatter(T1,yCalc1) //calculating the %age of scateering

ARTIFICIAL NEURAL NETWORK TECHNIQUE

file1 = ‘Bike-Ped-Auto-Counts-2014.xls’;

file2= ‘Bike-Ped-Auto-Counts-2015.xls’;

file3 = ‘Bike-Ped-Auto-Counts-2016.xls’;

file4= ‘Bikeway-counts—Counter-locations.xls’;

file5= ‘bike_ques5.xls’;

a=[3333]

4515

4249

2818

3384

4908

4211

.

.

.

3436

4190

3622

3269

2851

  % sTimestamp1=file(1);

 % sTimestamp2=file(2);

 % sTimestamp3=file(3);

 % sTimestamp4=file(4);

% sTimestamp5=file(5);

% sTimestamp6=file(6);

% sTimestamp7=file(7);

% sTimestamp8=file(8);

% sTimestamp9=file(9);

% Timestamp1 = cell2mat(sTimestamp1);

% Timestamp2 = cell2mat(sTimestamp2);

% Timestamp3 = cell2mat(sTimestamp3);

% Timestamp4 = cell2mat(sTimestamp4);

% Timestamp5 = cell2mat(sTimestamp5);

% Timestamp6 = cell2mat(sTimestamp6);

% Timestamp7 = cell2mat(sTimestamp7);

% Timestamp8 = cell2mat(sTimestamp8);

% Timestamp9 = cell2mat(sTimestamp9);

%

% plot(Timestamp9,Timestamp1,’*’)

% X=[ones(size(Timestamp9)) Timestamp9];

% beta=regress(Timestamp1,X)    //regression of Timestamp1 with respect to timestamp

% % scatter(Timestamp1,yCalc1) //Scaterring of the obtained image after calculations

CONCLUSIONS DRAWN

(I)Hence,from this approach of artificial neural network, visual problems may arise which hampers the data sets defined in a boundary.Moreover, challenges persistent to weather conditions may also prevail

(2)The approach has widely been used to prevent missing data as these are customed to sensors which has records of both coming and going transports.

(3)The predicted values are true to my knowledge as these are tested in prevailing conditions and has shown definite results during sensor failures.

Tensile Strength Analysis of Bolt Suppliers

Your firm works in construction and buys ASTM A325M8S bolts in very large numbers.

You are interested in purchasing bolts from different suppliers and have obtained and tested samples of 200 bolts from three suppliers: Allnutt, Boltzman, Coachers.

You have tested these in your new bolt testing machine, purchased after you had discovered your old bolt testing machine was performing erratically. (For the purposes of this question, you are safe to assume that the new machine works perfectly and that its results are in MPa.)

Your firm wants to ensure that the tensile strengths of the bolts it buys are

• as high as possible
• as consistent as possible (i.e., the variability of bolt strengths is as lowas possible)

It is critical that the tensile strength of these bolts exceeds 830MPa.

You should draw on the unit contentconcerning summary statistics to answer this question.

On the basis of the measurements recorded in Bolts.csv, which supplier (if any) would you recommend, and why?
(Provide any code or visualisations you use to justify your response.)

Edit your response to this question here.

filename = ‘Bolts.xls’;

[numbers, TEXT, file] = xlsread(filename);

T = cell2mat(st1)

t1=T(1:100)

t2=T(101:200)

t3=T(201:400)

for count1

limit=850;

for i=1:200

    if (t1(i,1))>=limit

    {

     count1=count1+1    

     else

     {

         count1+1

      }

end

     }

    end

for count2

for( i=1:200)

    if (t2(i,1))>=limit

{

     count2=count2+1   

}

     else

{

         count2=count2

}

     end

end

for count3

for( i=1:200)

    if (t3(i,1))>=limit

{

     count3=count3+1

}    

     else

{

         count3=count3

}

     end

        printf(‘Tensile strength  count >830 from Allnuttn’,count1);

        printf(‘Tensile strength  count >830 from Boltzmannn’,count2);

        printf(‘Tensile strength  count > 830 from Coachersn’,count3);

histfit(t1)

true

histfit(t2)

true

histfit(t3)

false

Tensile strengths  count more than 830 from Allnutt= 40.

Tensile strengths  count more than 830 from Boltzmann= 100.

Tensile strengths  count more than 830 from Coachers= 160(approx).

ILLUSTRATION 1

From statistical analysis it has been found that tensile strength of the Bolt supplied by Coacher is having the highest count compared to other two suppliers. And from the bar plot, we can conclude that consistency of bolt strength or the variability of strength from coacher is lower compared to other two sellers

The state government’s Department of the Environment and Heritage Protection are concerned about air pollution levels in Brisbane city. You have been provided with data for two sites, the Brisbane CBD and South Brisbane, for 2010 and 2016 in the following four files:

• brisbanecbd-aq-2010.csv
• brisbanecbd-aq-2016.csv
• southbrisbane-aq-2010.csv
• southbrisbane-aq-2016csv

You have been asked to advise the Government:

• If air pollution is related across the two sites, and if so, has this relationship changed between 2010 and 2016

Has the level of air pollution at either site changed from 2010 to 2016? Have these changes across the two sites been consistent?

You have been advised to focus on the PM10 data series.

You should draw on the unit contentconcerning summary statistics and correlation to answer this question.

(Provide any code or visualization’s you use to justify your response.)

Edit your response to this question here

filename = ‘brisbanecbd-aq-2010.xls’;

sheet = 1;

[numbers, TEXT, file] = xlsread(filename);

br1=file(:,7);

S1 = cell2mat(br1);

filename = ‘brisbanecbd-aq-2016.xls’;

sheet = 1;

[numbers, TEXT, file] = xlsread(filename);

br2=file(:,7);

S2 = cell2mat(br2);

filename = ‘southbrisbane-aq-2010.xls’;

sheet = 1;

[numbers, TEXT, file] = xlsread(filename);

PM10 Air Pollution Levels in Brisbane

br3=file(:,7);

S3 = cell2mat(br3);

filename = ‘southbrisbane-aq-2016.xls’;

sheet = 1;

[numbers, TEXT, file] = xlsread(filename);

br4=file(:,7);

S4 = cell2mat(br4); //concatenating the cells in the matrix

size(S1);

size(S2);

size(S3);

size(S4);

z=1:1:8760;

S1=S1(1:8760);

S2=S2(1:8760);

S3=S3(1:8760);

S4=S4(1:8760);

[S1(1:8760) S2(1:8760) S3(1:8760) S4(1:8760)];

hist(S1)//histogram(S1)

hold on

hist(S2)//histogram(S2)

Your engine testing company has been evaluating the relationship between engine capacity (in cubic cm) and particulate emissions (in particles per milliliter) across a range of engines. Now you have received results from tests of 100 ethanol-fuelled engines and your supervisor has sent you the following email about that:

Take a look at the emissions data for the 100 ethanol engines in thefile “Engines.csv” and write an email to your supervisor explaining your findings.You should draw on the unit content concerning correlation and regression to answer this question.
(Provide any code or visualisations you use to justify your response.)

Edit your response to this question here

filename = ‘Engine.xls’;

sheet = 1;

[numbers, TEXT, file] = xlsread(filename);

eng1=file(:,2);

eng2=file(:,3);

S1 = cell2mat(eng1);   //concatenating cells with the matrix

S2 = cell2mat(eng2);

[S1 S2] //scattering of the cells after the desired concatenation

scatter(S1,S2)

R = corcoeff(S1,S2) //calculating the corelation coefficient

corr(S1,S2)

fitlm(S1,S2) //adjusting the size to fit

Estimated Coefficients:

                   Estimate        SE        tStatpValue

                   _________    _________    ______    __________

    (Intercept)       90.767        4.237    21.423    9.3819e-39

    x1             -0.014376    0.0014379    -9.998    1.2227e-16

Number of observations: 100, Error degrees of freedom: 98

Root Mean Squared Error: 3.61

R-squared: 0.505,  Adjusted R-Squared 0.5

F-statistic vs. constant model: 100, p-value = 1.22e-16

You work for a construction firm who need to be able to accurately predict the compressive strength of concrete given variables including the concrete composition and its age. You are aware that the relationship between these different components and the concrete strength is complex, however you have been asked to investigate how well a simple linear regression model works for prediction. Using the provided data (Concrete_Data.xls), develop models to predict:

• Concrete strength from the single best indicator variable;
• Concrete strength from all variables.

With the second model, determine if any variables are not contributing significantly to the model, and what impact removing these has on prediction performance. Comment on the final model and its accuracy.

You should draw on the unit contentconcerning correlation and regression to answer this question.

(Provide any code or visualisations you use to justify your response.)

Edit your response to this question here

compressive strength, concrete composition and its age

filename = ‘Concrete_Data.xls’;

sheet = 1;

[numbers, TEXT, file] = xlsread(filename);

no1=file(1);

no2=file(2);

no3=file(3);

no4=file(4);

no5=file(5);

no6=file(6);

no7=file(7);

no8=file(8);

no9=file(9);

Timestamp1 = cell2mat(no1);

Timestamp2 = cell2mat(no2);

Timestamp3 = cell2mat(no3);

Timestamp4 = cell2mat(no4);

Timestamp5 = cell2mat(no5);

Timestamp6 = cell2mat(no6);

Timestamp7 = cell2mat(no7);

Timestamp8 = cell2mat(no8);

Timestamp9 = cell2mat(no9);

plot(T1,T9,’*’)

lsline

X=[ones(size(T9)) T9]; //adjusting the size from T1 To T9 beta=regress(T1,X)

% scatter(T1,yCalc1) //calculating the %age of scattering

solution:  

y = 0.086x + 13.445
R² = 0.2508

CORRL: 0.497832952

From statistical analysis it has been found that Cement (component 1)(kg in a m^3 mixture) having the highest impact on Concrete compressive strength(MPa, megapascals). It is holding positive linear correlation and the correlation coefficient is found to be 0.497832722. Blast Furnace Slag (component 2)(kg in a m^3 mixture) and Fly Ash (component 3)(kg in a m^3 mixture) has low effect on Concrete compressive strength(MPa, megapascals). Superplasticizer (component 5)(kg in a m^3 mixture has positive impact on  Concrete compressive strength(MPa, megapascals)

Brisbane City Council (BCC) is considering upgrades to the bikeway networks. They are using data they have gathered from sensors placed along the bike paths, which record the number of cyclists, to plan the upgrades, however there have been a number of sensor failures which have resulted in their dataset missing a number of entries. BCC have requested that you investigate if it possible to predict missing data from data gathered from other sensors on the bike path network.

You have been provided with three years worth of data (Bike-Ped-Auto-Counts-2014.csv, Bike-Ped-Auto-Counts-2015.csv, and Bike-Ped-Auto-Counts-2016.csv). As an initial investigation, you have been asked to consider only these data series:

• BicentennialBikewayCyclistsInbound
• GoBetweenBridgeCyclistsInbound
• KangarooPointBikewayCyclistsInbound
• NormanParkCyclistsInbound
• RiverwalkCyclistsInbound
• StoryBridgeWestCyclistsInbound

Using the three years data, you are to:

• Determine which counters are best suited to predicting the missing data in others (i.e. which, if any counters, could be used to predict BicentennialBikewayCyclistsInbound).
• Predict missing data where appropriate to generate a more “complete” database.
• Comment on the resulting corrected dataset. In particular:
  • What problems, if any, may arise from this approach?
  • How effective has this been in reducing missing data?
  • How trustworthy are the predicted values?

You should draw on the unit contentconcerning correlation and regression to answer this question.

(Provide any code or visualisations you use to justify your response.)

Edit your response to this question here

filename =’bike_ques.xls’;

sheet = 1;

[numbers, TEXT, file] = xlsread(filename);

no1=file(1);

no2=file(2);

no3=file(3);

no4=file(4);

no5=file(5);

no6=file(6);

no7=file(7);

no8=file(8);

no9=file(9);

Timestamp1 = cell2mat(no1);

Timestamp2 = cell2mat(no2);

Timestamp3 = cell2mat(no3);

Timestamp4 = cell2mat(no4);

Timestamp5 = cell2mat(no5);

Timestamp6 = cell2mat(no6);

Timestamp7 = cell2mat(no7);

Timestamp8 = cell2mat(no8);

Timestamp9 = cell2mat(no9);

plot(T1,T9,’*’)

lsline

X=[ones(size(T9)) T9]; //adjusting the size from T1 To T9

beta=regress(T1,X)

% scatter(T1,yCalc1) //calculating the %age of scateering

ARTIFICIAL NEURAL NETWORK TECHNIQUE

file1 = ‘Bike-Ped-Auto-Counts-2014.xls’;

file2= ‘Bike-Ped-Auto-Counts-2015.xls’;

file3 = ‘Bike-Ped-Auto-Counts-2016.xls’;

file4= ‘Bikeway-counts—Counter-locations.xls’;

file5= ‘bike_ques5.xls’;

a=[3333]

4515

4249

2818

3384

4908

4211

.

.

.

3436

4190

3622

3269

2851

  % sTimestamp1=file(1);

 % sTimestamp2=file(2);

 % sTimestamp3=file(3);

 % sTimestamp4=file(4);

% sTimestamp5=file(5);

% sTimestamp6=file(6);

% sTimestamp7=file(7);

% sTimestamp8=file(8);

% sTimestamp9=file(9);

% Timestamp1 = cell2mat(sTimestamp1);

% Timestamp2 = cell2mat(sTimestamp2);

% Timestamp3 = cell2mat(sTimestamp3);

% Timestamp4 = cell2mat(sTimestamp4);

% Timestamp5 = cell2mat(sTimestamp5);

% Timestamp6 = cell2mat(sTimestamp6);

% Timestamp7 = cell2mat(sTimestamp7);

% Timestamp8 = cell2mat(sTimestamp8);

% Timestamp9 = cell2mat(sTimestamp9);

%

% plot(Timestamp9,Timestamp1,’*’)

% X=[ones(size(Timestamp9)) Timestamp9];

% beta=regress(Timestamp1,X)    //regression of Timestamp1 with respect to timestamp

% % scatter(Timestamp1,yCalc1) //Scaterring of the obtained image after calculations

CONCLUSIONS DRAWN

(I)Hence,from this approach of artificial neural network, visual problems may arise which hampers the data sets defined in a boundary.Moreover, challenges persistent to weather conditions may also prevail

(2)The approach has widely been used to prevent missing data as these are customed to sensors which has records of both coming and going transports.

(3)The predicted values are true to my knowledge as these are tested in prevailing conditions and has shown definite results during sensor failures.