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6 Quick Instructions for Top Assignment Expert: 1. Please answer ALL questions below in American English spelling. (No format, just answer questions) 2. In the Analysis Problem section, please use...

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6
    Quick Instructions for Top Assignment Expert:
1. Please answer ALL questions below in American English spelling. (No format, just answer questions)
2. In the Analysis Problem section, please use the .csv link provided to download the data for analysis. Use any method you want (excel, R, etc.) to answer the questions.
3. The solution can be submitted by using this same document since you are just answering questions.
Short-Answer Problems
Some of these questions will appear on the short-answer part of the tests. As part of this homework, answer the following questions, usually just several sentences that include the definition.
Text, Ch 12
1. Identify the steps in the data preparation process.
2. Discuss the processes of validating and coding data.
3. Describe the processes of data entry and data cleaning.
4. Discuss the advantages of pre-coding questionnaires.
5. Compare a one-way tabulation vs. a two-way tabulation.
6. Explain the descriptive statistics that should be used with each type of scale.
Data Analysis
1. What does the business analyst hope to accomplish with a regression analysis? That is, what are the two goals of regression analysis?
1. What is ? What are the two primary situations in which it is applied?
1. How does the graph of X compare with Y vs the graph of X with ?
1. What is the meaning of the slope coefficient in = b0 + b1X1
1. In regression analysis computer output, there are one or more t-tests presented. Describe the purpose of the test(s), including the null and alternative hypotheses. What do you conclude if the associated p-value for a test is less than?
1. In computer output for regression analysis there are one or more confidence intervals presented. Describe the purpose of the confidence interval, and its interpretation.
1. What is the meaning of the residual variable e ?
7. What is the criterion of ordinary least squares regression to obtain the estimated model?
8. Model Fit: The standard deviation of the residuals to interpret model fit
9. Model Fit: R-squared as a relative index of fit
Analysis Problem
Note: This problem is similar to what you do for your project, use regression analysis to analyze the contribution of, here just one, product attribute to satisfaction. All responses obtained with a survey. Next week we do multiple regression where we have many product attributes in the equation, which is exactly the situation for your project, as well as the worked problem on the Final.
Consider a marketing survey of 253 customers of a restaurant called SFG.
Data: http:
web.pdx.edu/~ge
ing/data/SFGsfg.csv
Responses to the individual items are in a 7-pt Likert format, from 1 to 7. Assess the outcome variable of Satisfaction (x22) with the following item:
How satisfied are you with the SFG?
Not Satisfied        Very
At All     Satisfied
    1    2    3    4    5    6    7
What are the customer experiences that lead to customer satisfaction? One potential such variable is the customer’s perceived tastiness of the food. Consider the product attribute of taste, Tastiness (x18).
The food at the SFG tastes excellent.
Strongly    Strongly
Disagree    Agree
1    2    3    4    5    6    7
Research Question: To what extent does perceived Tastiness contribute to Overall Satisfaction at Restaurant SFG?
Do questions a through q and s, t, u, and x from the template.
Use the following information for Questions c and d.
    Scatterplot/Co
elation Matrix
a. Identify the response variable and the predictor variable(s).
. Show the scatterplot matrix (just one scatterplot for a single predictor) and co
elation coefficients of the relationship of each of the variables in the model with each other. From only this visual information, develop some intuition for the subsequent analysis.
i. Relevance: Do the predictor variables relate to the target (response) variable? Explain.
ii. Uniqueness: [If multiple predictor variables] Could collinearity be a problem? Explain.
iii. Model Selection: [If multiple predictor variables] Given the co
elations, what is the most likely candidate for the final model? Explain.
Estimated Model
c. Write the estimated regression model.
d. Specify and interpret the sample slope coefficient.
e. Manually calculate the fitted/predicted value for the given values of predictor variables X.
f. Manually calculate the associated residual. Interpret for the given values of predictor variables X and response variable y.
Hypothesis Test: Applied to the one specified predictor variable
g. Specify the null hypothesis and its alternative for the hypothesis test of the slope coefficient.
[answer with respect to the specifics of this analysis, e.g., not Predictor 1 but the actual name of each predictor in this specific analysis]
h. Show and label the calculation of how many (estimated) standard e
ors the estimated slope coefficient, b, is from the hypothesized population value.
[define the concept with the relevant numbers of this specific analysis, with or without a formula]
i. Include and apply the definition of the p-value with the relevant numbers for this specific analysis.
[include the relevant numbers in this specific analysis as an application of the general definition]
j. Specify the basis for the statistical decision for the hypothesis test and the resulting statistical conclusion for alpha=0.05.
[be specific with the numbers from this analysis as to the evaluation of the null hypothesis]
k. Hypothesis Test: Interpretation, as an executive summary you would report to management.
[applied to the relevant numbers of this specific analysis to generalize the results to the population, with no jargon like p-value or t-value or null hypothesis]
Confidence Interval: Applied to the one specified one predictor variable
l. Specify the value that the confidence interval estimates.
[do not provide the confidence interval, which is the estimate, not the value that it estimates]
m. Apply the definition of the 95% margin of e
or for its computation using the relevant numbers of this analysis with 2 approximating the t-cutoff.
[show the definition in words of the concept by applying the relevant numbers of this specific analysis, with or without a formula]
n. Show the computations of the 95% confidence interval illustrated with the specific numbers from this analysis.
[show the definition of the concept but apply the relevant numbers of this specific analysis, formula optional]
o. Confidence Interval: Interpretation, as an executive summary you would report to management.
[no jargon, which includes the phrase “slope coefficient”, nothing about hypothesis tests]
p. Demonstrate the consistency of the confidence interval and hypothesis test using the specific numbers for this analysis for both results.
[comparison includes the specifics of the numbers for this specific analysis for both inferential results]
Model Fit
q. Evaluate fit with the standard deviation of residuals.
. Evaluate fit with R2 and PRESS R2, including their comparison. Does this value indicate reasonable fit?
s. Show any potential outliers and explain why they are outliers.
Model Selection [if multiple predictor variables]
t. Consider all the predictor variables simultaneously. Based on the p-values of the slope coefficients, are any predictor variables much less useful for predicting the response variable (target)? Why or why not?
u. Any collinearity problems? Why or why not?
v. Based on this information and the best subset analysis, which model do you recommend? Why?
Prediction Intervals
w. For the 95% prediction interval of [response variable y] for [the values of predictor variables X], show the interval including its calculation (can approximate with the t-cutoff of 2).
x. Interpret the prediction interval.
Conclusion
y. What decision do you recommend to management based on these results?
c. value of the predictor variable: 4
d. value of the response variable: 5
Answered 2 days After Mar 01, 2023

Solution

Monica answered on Mar 04 2023
40 Votes
> li
ary(readxl)
fresh_file <- read_excel("C:/Users/dell/Desktop/fresh_file.xlsx")
View(fresh_file)
df = as.data.frame(fresh_file)
cor(df)
x1 x2 x3 x4 x5 x6
x1 1.00000000 0.193773125 0.1776329374 0.139720681 0.0748308610 0.23197007
x2 0.19377312 1.000000000 0.3073012480 -0.147645774 0.5524582689 0.35963852
x3 0.17763294 0.307301248 1.0000000000 -0.102878397 0.0597868380 0.78927346
x4 0.13972068 -0.147645774 -0.1028783969 1.000000000 0.1189897685 -0.22335345
x5 0.07483086 0.552458269 0.0597868380 0.118989769 1.0000000000 0.09983324
x6 0.23197007 0.359638519 0.7892734603 -0.223353450 0.0998332359 1.00000000
x7 0.15760888 0.300505476 0.5043859203 -0.025158323 0.0774153498 0.39220713
x8 0.24938108 -0.226660114 0.0001170273 0.765268074 -0.1356300070 -0.14928542
x9 0.64626087 0.143152405 0.1757359082 0.036088478 -0.0322518179 0.28754680
x10 0.18003215 0.003668685 0.0616813828 0.710445470 0.0931816984 -0.02612788
x11 0.85990945 0.012609640 0.0726366187 0.340744621 -0.0511518094 0.11488931
x12 0.97536499 0.163033129 0.1295663144 0.218528707 0.0353602830 0.17571032
x13 0.23293592 0.667759711 0.3986467615 -0.294352273 0.3362751448 0.46997423
x14 0.29650889 0.311222947 0.7958900352 -0.275130955 -0.0186194320 0.92984887
x15 -0.10721423 0.141738937 0.1150081512 -0.974184840 -0.1192733503 0.23868674
x16 0.24376761 0.361643268 0.7523012360 -0.299088104 0.1048364620 0.95288634
x17 0.18848231 0.317422611 0.2845016263 -0.200294808 0.1586212259 0.23571975
x18 -0.22998466 0.249319239 0.0070001907 -0.765151414 0.1453620392 0.16643766
x19 0.46173238 0.243350340 0.1121981616 -0.136919433 0.1613655722 0.22584417
x20 -0.15436339 -0.007413591 -0.0748209387 -0.675458629 -0.0955870072 0.01661756
x21 0.83294255 0.062008991 0.0853343174 0.272106823 0.0178669293 0.12190577
x22 0.36819730 0.187226350 0.2272088106 -0.592973400 -0.0802139936 0.38420891
x23 0.16996530 0.331971090 0.2338691585 -0.491040466 0.1792552251 0.40579404
x24 0.10697364 0.279856730 0.1989846276 -0.550213483 0.0748303352 0.34351608
x7 x8 x9 x10 x11 x12
x1 0.157608879 0.2493810831 0.64626087 0.180032147 0.85990945 0.97536499
x2 0.300505476 -0.2266601137 0.14315240 0.003668685 0.01260964 0.16303313
x3 0.504385920 0.0001170273 0.17573591 0.061681383 0.07263662 0.12956631
x4 -0.025158323 0.7652680742 0.03608848 0.710445470 0.34074462 0.21852871
x5 0.077415350 -0.1356300070 -0.03225182 0.093181698 -0.05115181 0.03536028
x6 0.392207135 -0.1492854219 0.28754680 -0.026127878 0.11488931 0.17571032
x7 1.000000000 0.0856740412 0.13960340 0.041320495 0.08285162 0.12306300
x8 0.085674041 1.0000000000 -0.01080829 0.744422355 0.37620402 0.28576525
x9 0.139603405 -0.0108082943 1.00000000 0.015102664 0.67265406 0.65884681
x10 0.041320495 0.7444223555 0.01510266 1.000000000 0.28389691 0.21433978
x11 0.082851618 0.3762040238 0.67265406 0.283896906 1.00000000 0.86570694
x12 0.123063004 0.2857652519 0.65884681 0.214339779 0.86570694 1.00000000
x13 0.244411070 -0.2965135853 0.27142365 -0.079322827 0.05223391 0.19226970
x14 0.397193402 -0.1137391055 0.29848047 -0.031535669 0.14022204 0.22678381
x15 0.014116335 -0.7715198480 0.03376542 -0.726707056 -0.31841052 -0.18900986
x16 0.382388272 -0.1877777587 0.27751833 -0.073648071 0.08197152 0.18172098
x17 0.461319315 -0.0836878048 0.08295721 -0.277372412 0.02538779 0.15108787
x18 -0.042815617 -0.9269614260 0.02399343 -0.736875711 -0.33927179 -0.26780189
x19 0.022945977 -0.2053752547 0.50197822 -0.095771858 0.31536158 0.47132858
x20 -0.021931648 -0.7292095368 0.02447444 -0.967484655 -0.23954084 -0.17799576
x21 0.092259325 0.3071589309 0.66212887 0.244632149 0.94143064 0.83593542
x22 0.078455863 -0.3846376288 0.31084724 -0.427024494 0.17277342 0.33093209
x23 0.128669046 -0.4411518727 0.33681797 -0.357145626 0.03604688 0.14576173
x24 0.089017417 -0.4496549500 0.29114771 -0.394878218 -0.08044061 0.06631448
x13 x14 x15 x16 x17 x18
x1 0.23293592 0.29650889 -0.107214228 0.24376761 0.18848231 -0.229984661
x2 0.66775971 0.31122295 0.141738937 0.36164327 0.31742261 0.249319239
x3 0.39864676 0.79589004 0.115008151 0.75230124 0.28450163 0.007000191
x4 -0.29435227 -0.27513096 -0.974184840 -0.29908810 -0.20029481 -0.765151414
x5 0.33627514 -0.01861943 -0.119273350 0.10483646 0.15862123 0.145362039
x6 0.46997423 0.92984887 0.238686737 0.95288634 0.23571975 0.166437656
x7 0.24441107 0.39719340 0.014116335 0.38238827 0.46131932 -0.042815617
x8 -0.29651359 -0.11373911 -0.771519848 -0.18777776 -0.08368780 -0.926961426
x9 0.27142365 0.29848047 0.033765425 0.27751833 0.08295721 0.023993430
x10 -0.07932283 -0.03153567 -0.726707056 -0.07364807 -0.27737241 -0.736875711
x11 0.05223391 0.14022204 -0.318410521 0.08197152 0.02538779 -0.339271789
x12 0.19226970 0.22678381 -0.189009856 0.18172098 0.15108787 -0.267801894
x13 1.00000000 0.45362769 0.329113881 0.46949340 0.45609005 0.331973983
x14 0.45362769 1.00000000 0.303591799 0.96176236 0.24977914 0.109668348
x15 0.32911388 0.30359180 1.000000000 0.32574350 0.21466702 0.770481270
x16 0.46949340 0.96176236 0.325743502 1.00000000 0.25286452 0.186544047
x17 0.45609005 0.24977914 0.214667023 0.25286452 1.00000000 0.146025497
x18 0.33197398 0.10966835 0.770481270 0.18654405 0.14602550 1.000000000
x19 0.31096649 0.25205220 0.176719013 0.26189014 0.08473295 0.190415525
x20 0.04475064 0.02026076 0.686457342 0.06022096 0.25892595 0.720975084
x21 0.10047941 0.14700469 -0.259894088 0.10100289 0.01739111 -0.273326195
x22 0.30859886 0.47298132 0.626624914 0.47949808 0.14811096 0.392684468
x23 0.42198201 0.40593951 0.511803032 0.46751025 0.26027873 0.474539086
x24 0.36097025 0.38975502 0.582297255 0.43339615 0.12298739 0.475526095
x19 x20 x21 x22 x23 x24
x1 0.46173238 -0.154363389 0.83294255 0.36819730 0.169965302 0.10697364
x2 0.24335034 -0.007413591 0.06200899 0.18722635 0.331971090 0.27985673
x3 0.11219816 -0.074820939 0.08533432 0.22720881 0.233869159 0.19898463
x4 -0.13691943 -0.675458629 0.27210682 -0.59297340 -0.491040466 -0.55021348
x5 0.16136557 -0.095587007 0.01786693 -0.08021399 0.179255225 0.07483034
x6 0.22584417 0.016617564 0.12190577 0.38420891 0.405794036 0.34351608
x7 0.02294598 -0.021931648 0.09225932 0.07845586 0.128669046 0.08901742
x8 -0.20537525 -0.729209537 0.30715893 -0.38463763 -0.441151873 -0.44965495
x9 0.50197822 0.024474436 0.66212887 0.31084724 0.336817965 0.29114771
x10 -0.09577186 -0.967484655 0.24463215 -0.42702449 -0.357145626 -0.39487822
x11 0.31536158 -0.239540844 0.94143064 0.17277342 0.036046884 -0.08044061
x12 0.47132858 -0.177995757 0.83593542 0.33093209 0.145761729 0.06631448
x13 0.31096649 0.044750637 0.10047941 0.30859886 0.421982010 0.36097025
x14 0.25205220 0.020260763 0.14700469 0.47298132 0.405939509 0.38975502
x15 0.17671901 0.686457342 -0.25989409 0.62662491 0.511803032 0.58229726
x16 0.26189014 0.060220965 0.10100289 0.47949808 0.467510250 0.43339615
x17 0.08473295 0.258925954 0.01739111 0.14811096 0.260278732 0.12298739
x18 0.19041553 0.720975084 -0.27332619 0.39268447 0.474539086 0.47552609
x19 1.00000000 0.111792872 0.39031081 0.34848919 0.335210163 0.32427780
x20 0.11179287 1.000000000 -0.19570989 0.42958162 0.382027433 0.39441997
x21 0.39031081 -0.195709891 1.00000000 0.21662003 0.086457659 -0.02316703
x22 0.34848919 0.429581621 0.21662003 1.00000000 0.791176157 0.77648184
x23 0.33521016 0.382027433 0.08645766 0.79117616 1.000000000 0.83565060
x24 0.32427780 0.394419973 -0.02316703 0.77648184 0.835650597 1.00000000
x25 x26 x27 x28 x29 x30
x1 0.19870390 -0.182474037 -0.050076666 0.189374709 0.085235817 -0.23580649
x2 0.32422116 -0.102239710 0.020661214 0.085393620 0.015169640 -0.20766400
x3 0.16023603 0.101520318 0.078186512 -0.133019560 -0.055351611 -0.27794968
x4 -0.48376260 -0.183190048 -0.195283956 -0.009756206 0.323219799 0.56363221
x5 -0.07364828 0.003096553 -0.070338467 -0.044634627 0.066057503 0.02273206
x6 0.32315571 0.023693949 0.142798276 -0.094862876 -0.049688982 -0.39804217
x7 0.07178883 -0.015980077 -0.005414514 -0.002848342 0.034114729 -0.18962974
x8 -0.42588042 -0.027455758 -0.138570945 0.002537925 0.110256814 0.39259318
x9 0.34716561 -0.302777669 -0.034631085 0.180928881 0.195095775 -0.23201808
x10 -0.30020419 -0.032492445 -0.132097034 -0.052387091 0.127577567 0.43216834
x11 0.07186245 -0.257791105 -0.051780375 0.167014988 0.184019343 -0.06917158
x12 0.22194110 -0.270502416 -0.050866779 0.209356197 0.165201128 -0.15851928
x13 0.52585164 -0.146354218 0.027293079 0.150215428 ...
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