Could you please help interpret this SPSS output?a) Determine the regression equation. vegression IVariables Entered/RamovadVariablesRemovedVariablesModelEnteredMethodFloor area insq mts,Branches ofmultiple stere,1Entermedianhousing costin $k within5km radius,Number ofemployees inbranch,Distance inkms fromnearestcompetitor,Number ofcarparkinga. Dependent Variable: Value of monthly sales in$k in 2007b. All requested variables entered.Model SummeryAdjusted RSquare.968Std. Error ofR Square991 983ModelRthe Estimate1537.69721a. Predictors: (Constant), Floor area in sq mts, Branches ofmultiple stere, median housing cost in $k within 5km radius,Number of employees in branch, Distance in kms fromnearest competitor, Number of carparking spacesb. Dependent Variable: Value of monthly sales in $k in 2007ANOVA:Sum ofModelSquaresdfMean SquareFSig.<.001Regression 114716546.936 19119424.4966.1307Residual2023827.99177 289118.284Total13116740374.929a. Dependent Variable: Value of monthly sales in $k in 2007b. Predictors: (Constant), Floor area in sq mts, Branches of multiple stere, medianhousing cost in Sk within 5km radius, Number of employees in branch, Distance inkms from nearest competitor, Number of carparking spacesCoofficiente:UnstandardizedStandardizedCoefficientsCoefficientsB Std. Error505.6761215.593ModelBBetat(Constant)Sig6901.416Number of employees in83.206.13191.4081.099.308branchNumber of carparking28.44211.355.5212.505.041spacesDistance in kms from-9.787145.917-.008-.067.948nearest competitor-.9953.910-.022-.254.807median housing cost inSk within 5km radiusBranches of multiple4.30253.438.006.081.938storeFloor area in sa mts7002583962.713030Coafficiente:Collinearity StatisticsToleranceCorrelationsZero-order PartialModel1PartVIF(Constant)Number of employees in.892.383.055.1745.747branchNumber of carparking.975.687.125.05717.441spacesDistance in kms from.764-.025-.003.1705.898nearest competitormedian housing cost inSk within 5km radiusBranches of multiple.681-.096-.013.3352.988320.030.0044452.247storeFloor area in sq mts.954716.135.1168.596 a. Dependent Variable: Value of monthly sales in $k in 2007Collinearity Diagrastice.Variance ProportionsNumber ofNumber ofEigenvaluConditionemployees incarparkingModel DimensionIndex(Constant)branch6.492spaces.001.000.00.002.2395.217.01.00.013.1666.246.02.01.00.06210.194.00.04.005.02615.955.02.37.01.01024.8802234.59.00438.306.73.24.38Collinearity Diegnastice:Variance ProportionsDistance inmediankms fromhousing costin $k within5km radius multiple store.00Branches ofFloor area innearestModel Dimension competitorsq mts.00.00.00200.00.13.023.02.01.27.01.38.00.10.06.01.01.11.46.07.09.11.187.51.88.28.27a. Dependent Variable: Value of monthly sales in $k in 2007Residuals StetisticeStd.Mean2652.0798 11307.1230 5826,9286 2970.58071Minimum MaximumDeviationPredicted Value14Residual-707.65735 765.92017.00000 394.5618814Std. Predicted-1.0691.845.0001.00014ValueStd. Residual-1.3161.424.000.73414a. Dependent Variable: Value of monthly sales in $k in 2007ChartsHistogramDependent Variable: Value of monthly sales in Sk in 2007S. DesgrN14-1500Regression Standardized ResidualScatterplotDependent Variable: Value of monthly sales in $k in 200718Regression Standardized Predicted ValueRegression Standardized Residual

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Answered: Could you please help interpret this…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Researchers at a large nutrition and weight management company are trying to build a model to predict a person’s body fat percentage from an array of variables. A variables selection method is used to build a regression model. SAS output for the final model is given in photo. Question: What percentage of the variation in percent body fat remains unexplained, even after introducing weight and abdomen circumference into the model, and then also determine the interpretation of the slope for weight?
A local University conducted a survey of over 2,000 MBA alumni to explore the issue of work-life balance. Each participant received a score ranging from 0 to 100, with lower scores indicating a higher imbalance between work and life. A sample of the data is available below. Let x=average number of hours worked per week and y= work-life balance scale score for each MBA alumnus. Investigate the link between these two variables by conducting a complete simple linear regression analysis of the data. Summarize your findings. Hours WLB Score 50 74.09 45 72.45 50 52.93 55 44.33 50 69.15 60 54.79 55 56.26 60 20.44 55 6-.64 50 69.15 70 29.16 45 72.45 40 33.25 40 32.18 45 45.76 a. What is the test statistic for the hypotheses? t=______ b. What is the p-value for the test statistic? p-value=________ c. What is the value for the coeffiecent of determination r^2? r^2=________
A cafe company wants to determine how the money they spend on Google ads impacts their monthly revenue. Over 6 consecutive months, they vary the amount they spend on their Ads (in $) and record the associated revenue (in $) for each month. The data is shown below: l Revenue 50 427 75 472 100 467 125 529 150 518 175 543 A) Develop a regression equation for predicting monthly revenue based on the amount spent with Ads. What is the y-intercept? B) What is the sample correlation between these two variables? C) What is the slope of your regression equation? Give your answer to two decimal places. D) Using a 0.05 level of significance, does this regression equation appear to have any value for predicting revenue based on Ads?
When should a regression model not be used to make a prediction?
Explain what a residual is and how this relates to the best-fit regression model.
How to know if obtaining a regression equation for the data appear reasonable?
A local University conducted a survey of over 2,000 MBA alumni to explore the issue of work-life balance. Each participant received a score ranging from 0 to 100, with lower scores indicating a higher imbalance between work and life. A sample of the data is available below. Let x = average number of hours worked per week and y=work-life balance scale score for each MBA alumnus. Investigate the link between these two variables by conducting a complete simple linear regression analysis of the data. Summarize your findings. E Click the icon to view the data. The least squares regression equation is y =+ (Ox. (Round to two decimal places as needed.) Revenue and Message Rate for Recent Movies Check the usefulness of the hypothesized model. What are the hypotheses to test? O A. H Bo =0 against H: Bo #0 Hours WLB Score 50 75.22 B. H: B, #0 against H: B, =0 45 78.45 OC. H B, = 0 against H B, 0 50 49.68 55 40.11 OD. H Bo#0 against H: Bo =0 50 70.41 60 55.91 Determine the estimate of the…
Using your dataset, run a regression of Y=GPA and X=# Friends.(do not need your actual data, just the regression results)a) State what this regression is attempting to analyze. “By running this regression, we areattempting to show.....”b) Write out the regression equation and describe what it shows (if Friends increase by 1, then. . . ).c) Find your hypothesized GPA when the # friends equals 17.d) Is the slope of # of Friends significantly different from zero?Include Ho, Ha, decision rule, t statistic from table, tc, decision, and conclusion.e) Is the r-squared of # of Friends significantly different from zero?Include Ho, Ha, decision rule, F statistic from table, Fc, decision, and conclusion.
The table lists the average monthly cost to workers for family health insurance for various years. Year, x Average Monthly Cost to Workers for Family Health Insurance $298 a) Use a graphing calculator to fit a regression line to the data. b) Predict the average monthly cost to workers for family health insurance in 2020, and compare the value with $493.3, which is obtained using the points (1,340) and (4,386). c) Find the correlation coefficient for the regression line, and determine whether the line fits the data closely. 2009, 0 2010, 1 2011, 2 340 348 2012, 3 367 2013, 4 2014, 5 386 406 a) The linear equation of the regression line that best models the data is y =x+. (Round to the nearest hundredth as needed.) b) The average monthly cost to workers for family health insurance in 2020 is predicted to be $ (Round to the nearest cent as needed.) Compare the above obtained value with $493.3 This value is $ $493.3 c) The correlation coefficient is (Round to the nearest thousandth as…

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Could you please help interpret this SPSS output? a) Determine the regression equation.