Durbin-Watson StatisticDurbin-Watson Statistic = 2.18902Model SummaryS R-sq R-sq(adj) R-sq(pred)31.1054 89.47% 87.37% 79.56%Analysis of VarianceSourceDF Adj SS Adj MS F-Value P-ValueRegression 1 41109 41109.1 42.490.001Calories1 41109 41109.1 42.490.0015 4838 967.56 45947ErrorTotalCoefficientsTermCoef SE Coef T-Value P-Value VIFConstant -487144-3.37 0.020Calories 5.3640.8236.520.001 1.00In the box below summarise the results of the analysis, commenting on the type of relationship that exists and whether it is statisticallysignificant. Give a summary of the the regression analysis carried out making reference to the regression equation and regression co-efficent. Back your answer up with relevant data from the tables above. A researcher took a random sample of beef hotdogs and measured the amount of sodium (in mg) and calories contained in them. Theresults of a correlation and regression analysis are shown in the tables below:Correlation: Calories, Sodium ContentMatrix Plot of Calories, SodiumPearson Correlation600500400300r = 0.946 p = 0.001%3D150160170180190CaloriesPairwise Pearson CorrelationsSample 1 Sample 2 N Correlation 95% CI for p P-ValueSodium Calories 70.946 (0.670, 0.992) 0.001Regression AnalysisDurbin-Watson StatisticDurbin-Watson Statistic = 2.18902Sodium

A researcher took a random sample of beef hotdogs and measured the amount of sodium (in mg) and calories contained in them. The results of a correlation and regression analysis are shown in the tables below: Correlation: Calories, Sodium Content Matrix Plot of Calories, Sodium Pearson Correlation 600 500 400 300 r = 0.946 p = 0.001 150 160 170 180 190 Calories Pairwise Pearson Correlations Sample 1 Sample 2 N Correlation 95% CI for p P-Value Sodium Calories 7 0.946 (0.670, 0.992) 0.001 Regression Analysis Durbin-Watson Statistic Durbin-Watson Statistic = 2.18902 Sodium

A researcher took a random sample of beef hotdogs and measured the amount of sodium (in mg) and calories contained in them. The results of a correlation and regression analysis are shown in the tables below: Correlation: Calories, Sodium Content Matrix Plot of Calories, Sodium Pearson Correlation 600 500 400 300 r = 0.946 p = 0.001 150 160 170 180 190 Calories Pairwise Pearson Correlations Sample 1 Sample 2 N Correlation 95% CI for p P-Value Sodium Calories 7 0.946 (0.670, 0.992) 0.001 Regression Analysis Durbin-Watson Statistic Durbin-Watson Statistic = 2.18902 Sodium

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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Listed below are paired data consisting of amounts spent on advertising (in millions of dollars) and the profits (in millions of dollars). Determine if there is a significant negative linear correlation between advertising cost and profit . Use a significance level of 0.01 and round all values to 4 decimal places. Advertising Cost Profit 17 4 15 20 6. 28 7 17 8 29 Ho: p = 0 На: р < 0 Find the Linear Correlation Coefficient r = Find the p-value p-value = The p-value is Less than (or equal to) a O Greater than a The p-value leads to a decision to Ассept Ho Reject Ho O Do Not Reject Ho The conclusion is O There is a significant negative linear correlation between advertising expense and profit. There is a significant positive linear correlation between advertising expense and profit. O There is a significant linear correlation between advertising expense and profit. O There is insufficient evidence to make a conclusion about the linear correlation between advertising expense and profit.
The Minitab output shown below was obtained by using paired data consisting of weights (in Ib) of 27 cars and their highway fuel consumption amounts (in mi/gal). Along with the paired sample data, Minitab was also given a car weight of 4500 lb to be used for predicting the highway fuel consumption amount. Use the information provided in the display to determine the value of the linear correlation coefficient. (Be careful to correctly identify the sign of the correlation coefficient.) Given that there are 27 pairs of data, is there sufficient evidence to support a claim of linear correlation between the weights of cars and their highway fuel consumption amounts? Click the icon to view the Minitab display. Minitab output The linear correlation coefficient is. (Round to three decimal places as needed.) The regression equation is Highway = 50.4- 0.00535 Weight %3D Predictor Сoef SE Coef T Constant 50.383 2.732 17.75 0.000 Weight - 0.0053502 0.0007856 -7.55 0.000 S= 2.17876 R-Sq = 65.1% R-…
Listed below are annual data for various years. The data are weights (metric tons) of imported lemons and car crash fatality rates per 100,000 population. Construct a scatterplot, find the value of the linear correlation coefficient r, and find the P-value using a = 0.05. Is there sufficient evidence to conclude that there is a linear correlation between lemon imports and crash fatality rates? Do the results suggest that imported lemons cause car fatalities? Lemon Imports Crash Fatality Rate 229 266 359 480 530 15.9 15.6 15.5 15.3 14.9 Construct a scatterplot. Choose the correct graph below. OA. В. Ос. D. Ay 17- Ay 17- Ay 17- Ay 17- 16- 16- 16- 16- 15- 15- 15- 15- 14+ 14+ 14- 14+ 200 400 600 200 400 600 200 400 600 200 400 600 The linear correlation coefficient r is (Round to three decimal places as needed.)
Listed below are annual data for various years. The data are weights (metric tons) of imported lemons and car crash fatality rates per 100,000 population. Construct a scatterplot, find the value of the linear correlation coefficient r, and find the P-value using a = 0.05. Is there sufficient evidence to conclude that there is a linear correlation between lemon imports and crash fatality rates? Do the results suggest that imported lemons cause car fatalities? Lemon Imports Crash Fatality Rate 231 266 359 480 532 15.9 15.7 15.4 15.2 14.8 Ay 17- Ay 17- Ay 17+ Ay 17- 16- 16- 16- 16- 15- 15- 15- 15- X 14- 14+ 14- 14- 200 400 600 200 400 600 200 400 600 200 400 600 The linear correlation coefficient r is (Round to three decimal places as needed.) The P-value is (Round to three decimal places as needed.) Because the P-value is than the significance level 0.05, there sufficient evidence to support the claim that there is a linear correlation between lemon imports and crash fatality rates for a…
The data obtained in a study on the number of absences in virtual class and the final grades of seven randomly selected students from a mathematics class. The data are shown below. To determine the significance of the correlation coefficient at 0.05 level of significance, what is the calculated or computed value (CV)? Number of absences x Final in a virtual class grade y 82 6 86 15 43 9. 74 58 5 90 78 a. -1.59 b. 2.32 C. -2.32 d. 1.59
Listed below are annual data for various years. The data are weights (metric tons) of imported lemons and car crash fatality rates per 100,000 population. Construct a scatterplot, find the value of the linear correlation coefficient r, and find the P-value using a = 0.05. Is there sufficient evidence to conclude that there is a linear correlation between lemon imports and crash fatality rates? Do the results suggest that imported lemons cause car fatalities? Lemon Imports Crash Fatality Rate 228 264 358 482 531 15.9 15.7 15.5 15.3 14.9
The Minitab output shown below was obtained by using paired data consisting of weights (in lb) of 31 cars and their highway fuel consumption amounts (in mi/gal). Along with the paired sample data, Minitab was also given a car weight of 4000 lb to be used for predicting the highway fuel consumption amount. Use the information provided in the display to determine the value of the linear correlation coefficient. (Be careful to correctly identify the sign of the correlation coefficient.) Given that there are 31 pairs of data, is there sufficient evidence to support a claim of linear correlation between the weights of cars and their highway fuel consumption amounts? Click the icon to view the Minitab display. The linear correlation coefficient is (Round to three decimal places as needed.) Is there sufficient evidence to support a claim of linear correlation? Yes O No Minitab output The regression equation is Highway = 50.8 -0.00508 Weight Predictor Coef SE Coef T P Constant 50.772 2.793…
The Minitab output shown below was obtained by using paired data consisting of weights (in lb) of 26 cars and their highway fuel consumption amounts (in mi/gal). Along with the paired sample data, Minitab was also given a car weight of 3000 lb to be used for predicting the highway fuel consumption amount. Use the information provided in the display to determine the value of the linear correlation coefficient. (Be careful to correctly identify the sign of the correlation coefficient.) Given that there are 26 pairs of data, is there sufficient evidence to support a claim of linear correlation between the weights of cars and their highway fuel consumption amounts? Click the icon to view the Minitab display. The linear correlation coefficient is (Round to three decimal places as needed.) Minitab output The regression equation is Highway = 50.3 -0.00539 Weight Predictor Coef SE Coef Constant 50.288 2.998 Weight -0.0053868 0.0007773 |S=2.11773 R-Sq=64.0% R-Sq(adj) = 60.9% Predicted Values…
Listed below are annual data for various years. The data are weights (metric tons) of imported lemons and car crash fatality rates per 100,000 population. Construct a scatterplot, find the value of the linear correlation coefficient r, and find the P-value using a= 0.05. Is there sufficient evidence to conclude that there is a linear correlation between lemon imports and crash fatality rates? Do the results suggest that imported lemons cause car fatalities? Lemon Imports Crash Fatality Rate 266 15.7 228 358 484 531 15.8 15.5 15.2 14.8 What are the null and alternative hypotheses? O B. Ho: p=0 O A. Ho: p#0 H1:p=0 H1:p0 H,: p#0 Construct a scatterplot. Choose the correct graph below. OA. B. Oc. OD. Ay 17- Ay 17- AY 17- Ay 17- 16- Q 16- 16- 16- 15- 15- 15- 15- 14- 14+ 14- 14- 200 400 600 200 400 600 200 400 6ỏ0 200 400 600 The linear correlation coefficient is r= (Round to three decimal places as needed.)
Listed below are paired data consisting of amounts spent on advertising (in millions of dollars) and the profits (in millions of dollars). Determine if there is a significant linear correlation between advertising cost and profit Use a significance level of 0.01 and round all values to 4 decimal places. Advertising Cost Profit 3 21 4 16 5 29 17 7 28 8 21 Ho: p = 0 Ha: p +0 Find the Linear Correlation Coefficient r= Find the p-value p-value = The p-value is Less than (or equal to) a OGreater than a The p-value leads to a decision to Do Not Reject Ho OReject Ho OAccept Ho
The age and prices of 11 Nissan cars given in the following data: Age (in years) 5 4 6 5 5 5 6 6 2 7 7 Price (in $100s) 85 103 70 82 89 98 66 95 169 70 48 a. Obtain the correlation coefficient between the age and price of the cars. Interpret the value of the correlation coefficient. b. Obtain a regression equation of Price (dependent variable) on Age (Independent variable). c. What will be the Price of the car after 3 years? d. Find the coefficient of determination. Determine the percent of variation that is explained by the regression equation clearly.
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