1. Using 1% & 5% alphas, test the overall significance of the model on excel. ASUMMARY1 OUTPUTWN234 Multiple R5 R SquareRegression Statistics7Adjusted R6 SquareStandard ErrorObservations8910 ANOVA1112 Regression13 Residual14 Total151617 Intercept18 fixed acidity;volatile acidity1920 citric acidresidual sugar2122 chloridesfree sulfur23 dioxidetotal sulfurB24 dioxide25 density26 pH27 sulphates28 alcohol000.6004595770.3605517030.3561194840.648011208df1599сCoefficientsSSD11 375.7544028 34.159491161587 666.4107004 0.4199185261598 1042.165103StandardError21.96520845 21.1945750.024990553 0.025948502-1.083590259 0.12110128MSt StatEFP-valueF81.3479022 1.7914E-145Significance F-1.874225158 0.419283205 -4.470069717 8.37395E-060.004361333 0.002171292 2.008635263 0.044744951Lower 95%GUpper 95%HLower 95.0%|Upper 95.0%1.036359939 0.300192136 -19.60710094 63.53751785 -19.60710094 63.537517850.963082682 0.335652752 -0.025906394 0.075887499 -0.025906394 0.075887499-8.947801896 9.87236E-19 -1.321125565 -0.846054953 -1.321125565 -0.846054953-0.182563948 0.147176188 -1.240444878 0.214994246 -0.471244142 0.106116245 -0.471244142 0.1061162450.01633127 0.015002096 1.088599183 0.276495961 -0.013094741 0.04575728 -0.013094741 0.04575728-2.69663236 -1.051817956 -2.69663236 -1.0518179560.000102431 0.008620235 0.000102431 0.008620235-0.00326458 0.000728729 -4.47982984 8.00461E-06 -0.004693951 -0.001835208 -0.004693951 -0.001835208-17.88116384 21.63309988 -0.826565029 0.408607897 -60.31362221 24.55129454 -60.31362221 24.55129454-0.413653144 0.191597361 -2.158970991 0.031001886 -0.789463688 -0.0378426 -0.7894636880.916334413 0.114337465 8.014297061 2.12723E-15 0.692066057 1.140602768 0.6920660570.276197699 0.026483586 10.42901431 1.12303E-24 0.224251206 0.328144192 0.224251206-0.03784261.1406027680.328144192

It is needed to test the significance of the overall model.

Answered: Using 1% & 5% alphas, test the overall…

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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The managing partner of an advertising agency believes that his company's sales are related to the industry sales. He uses Microsoft Excel’s Data Analysis tool to analyze the last 4 years of quarterly data (i.e., n = 16) with the following results: Regression StatisticsMultiple R 0.802R Square 0.643Adjusted R Square 0.618Standard Error SYX 0.9224Observations 16 ANOVA df SS MS F Sig.FRegression 1 21.497 21.497 25.27 0.000Error 14 11.912 0.851Total 15 33.409 Predictor Coef StdError tStat P-valueIntercept 3.962 1.440 2.75 0.016Industry 0.040451 0.008048 5.03 0.000 Durbin-Watson Statistic 1.59 Referring to Table 13-5, the prediction for a quarter in which X = 80 is Y -hat = ________.
A business is evaluating their advertising budget, and wishes to determine the relationship between advertising dollars spent and changes in revenue. Below is the output from their regression. SUMMARY OUTPUT Regression Statistics Multiple R 0.95 R Square 0.90 Adjusted R Square 0.82 Standard Error 0.82 Observations 8 ANOVA df SS MS F Significance F Regression 3 23.188 7.729 11.505 0.020 Residual 4 2.687 0.672 Total 7 25.875 Coefficients Std Error t Stat P-value Lower 95% Upper 95% Intercept 83.91 2.03 41.36 0.00 78.28 89.54 TV ($k) 1.96 0.48 4.10 0.01…
A business is evaluating their advertising budget, and wishes to determine the relationship between advertising dollars spent and changes in revenue. Below is the output from their regression. SUMMARY OUTPUT Regression Statistics Multiple R 0.95 R Square 0.90 Adjusted R Square 0.82 Standard Error 0.82 Observations 8 ANOVA df SS MS F Significance F Regression 3 23.188 7.729 11.505 0.020 Residual 4 2.687 0.672 Total 7 25.875 Coefficients Std Error t Stat P-value Lower 95% Upper 95% Intercept 83.91 2.03 41.36 0.00 78.28 89.54 TV ($k) 1.96 0.48 4.10 0.01…
A business is evaluating their advertising budget, and wishes to determine the relationship between advertising dollars spent and changes in revenue. Below is the output from their regression. SUMMARY OUTPUT Regression Statistics Multiple R 0.95 R Square 0.90 Adjusted R Square 0.82 Standard Error 0.82 Observations 8 ANOVA df SS MS F Significance F Regression 3 23.188 7.729 11.505 0.020 Residual 4 2.687 0.672 Total 7 25.875 Coefficients Std Error t Stat P-value Lower 95% Upper 95% Intercept 83.91 2.03 41.36 0.00 78.28 89.54 TV ($k) 1.96 0.48 4.10 0.01…
A researcher’s results are shown below using Femlab (labor force participation rate among females) to try to predict Cancer (death rate per 100,000 population due to cancer) in the 50 U.S. states. Regression Statistics Multiple R 0.313422848 R Square 0.098233882 Adjusted R Square 0.079447088 Standard Error 32.07003698 Observations 50 Variable Coefficients Standard Error t Stat Intercept 343.619889 61.0823514 5.62552 Femlab –2.2833659 0.99855319 –2.28667 Which statement is valid regarding the relationship between Femlab and Cancer? Multiple Choice At the .05 level of significance, there isn’t enough evidence to say the two variables are related. This model explains about 10 percent of the variation in state cancer rates. If your sister starts working, the cancer rate in your state will decline. A rise in female labor participation rate will cause the cancer rate to decrease within a state.
9. This small dataset reports the Average Class Size, Combined SAT score, and the pct of the class that Attends 4 Year Colleges. Estimate a regression to answer the following. Each additional student's increase in avg class size is associated with a _____________ increase in SAT scores. Round your answer to two decimal places. District AverageClass Size CombinedSAT Score % Attend4-Year College Blue Springs, MO 25 1083 74 Garden City, NY 18 997 77 Indianapolis, IN 30 716 40 Newport Beach, CA 26 977 51 Novi, MI 20 980 53 Piedmont, CA 28 1042 75 Pittsburg, PA 21 983 66 Scarsdale, NY 20 1110 87 Wayne, PA 22 1040 85 Weston, MA 21 1031 89 Farmingdale, NY 22 947 81 Mamaroneck, NY 20 1000 69 Mayfield, OH 24 1003 48 Morristown, NJ 22 972 64 New Rochellle, NY 23 1039 55 Newtown Square, PA 17 963 79 Omaha, NE 23 1059 81 Shaker Heights, OH 23 940 82
Engro group, who recently sold its Engro foods start-up for a multi-million-rupees sum, islooking for another investment for fresh capital. It is considering an investment in coal (X) andsolar (Y) power plant. For that they had a collected data on 5 different characteristics. Amandahas applied k-means clustering to this data for k = 2. Given the following data:Table 5.1 Observations X Y 1 20 18 2 8 20 3 36 26 4 22 12 5 14 4 a) Which value of k is the most appropriate to categorize these investments? Justify yourchoice with calculations.b) The two cluster centers after the first round of execution. c) The final two clusters. d) Visually represent the clusters of both parts (b) and (c).
Louis Katz, a cost accountant at Papalote Plastics, Inc. (PPI), is analyzing the manufacturing costs of a molded plastic telephone handset produced by PPI. Louis's independent variable is production lot size (in 1,000's of units), and his dependent variable is the total cost of the lot (in $100's). Regression analysis of the data yielded the following tables. Coefficients Standard Error t Statistic p-value Intercept 3.996 1.161268 3.441065 0.004885 x 0.358 0.102397 3.496205 0.004413 Source df SS MS F Se = 0.898 Regression 1 9.858769 9.858769 12.22345 r2 = 0.526341 Residual 11 8.872 0.806545 Total 12 18.73077 Using a = 0.05, Louis should ________________.
Problem I ONA model is developed for forecasting of sale and the effects of three independent variables , advertising expenditure (X1), Price (X2), and time (X3) resulted in the following. Regression Statistics Standard Error 232.29 Table 1: ANOVA df SS MS F Regression 3 53184931.86 ? ? Residual ? 1133108.30 ? Total 24 54318040.16 Table 2: regression Coefficients Standard Error t Stat Intercept 927.23 1229.86 ? Advertising (X1) 1.02 3.09 ? Price (X2) 15.61 5.62 ? Time (X3) 170.53 28.18 ? d. At 95% confidence, determine whether or not the over all regression model is…
A farming syndicate intends to analyze the effect of per capita income (EUR thousand/capita/year), price of potato (EUR /kilogram), and regional status (City = 1 and Regency = 0) on average potato consumption per capita in a week (kilogram/capita/week) in Ulster Province of Ireland (consists of 6 cities and 29 regencies) in 2009. The regression results are as follows. Summary Outpot Regression Analysis Multiple R 0.9542 R Square 0.9106 Adjusted R Square ......... Standard Error 0.0585 Observations 35 ANOVA df SS MS F Significance F Regression ......... ......... ......... ......... 0.0000 Residual ......... 0.1060 ......... Total ......... 1.1860 Coefficients Standard Error t Stat P-value Intercept 0.1534 0.1057 ......... 0.1567 Income per Capita 0.1399 0.0091 ......... 0.0000 Price -0.0066 0.0075 ......... 0.3831 Regional Status -0.2424 0.0397 ......... 0.0000 a. Find the missing values in the tables. b. Formulate the…
The following problems can be threats to the validity of a regression discontinuity analysis, with the exception of: a. jumps in covariates. b. manipulation of the assignment variable. c. nonlinear relationship between the assignment and the outcome Variable. d. misallocation of treatment.
10. This small dataset reports the Average Class Size, Combined SAT score, and the pct of the class that Attends 4 Year Colleges. Estimate a regression to answer the following. Each additional student's increase in avg class size is associated with a _____________ increase in the attendance rate in 4 year colleges. Round your answer to two decimal places. District AverageClass Size CombinedSAT Score % Attend4-Year College Blue Springs, MO 25 1083 74 Garden City, NY 18 997 77 Indianapolis, IN 30 716 40 Newport Beach, CA 26 977 51 Novi, MI 20 980 53 Piedmont, CA 28 1042 75 Pittsburg, PA 21 983 66 Scarsdale, NY 20 1110 87 Wayne, PA 22 1040 85 Weston, MA 21 1031 89 Farmingdale, NY 22 947 81 Mamaroneck, NY 20 1000 69 Mayfield, OH 24 1003 48 Morristown, NJ 22 972 64 New Rochellle, NY 23 1039 55 Newtown Square, PA 17 963 79 Omaha, NE 23 1059 81 Shaker Heights, OH 23 940 82

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