Q10. Find a 95% confidence interval for the regression coefficient. x = humidity of air [%]. y = expansion of gelatin [%] 10 20 30 40 y 0.8 1.6 2.3 2.8
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![Q10. Find a 95% confidence interval for the regression coefficient.
x = humidity of air [%]. y = expansion of gelatin [%]
10
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y
0.8
1.6
2.3
2.8](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F29897633-f561-47b0-98f1-1d929a9024ca%2F10c8f8ff-c3d8-435f-8d9c-8a5c531b0488%2Fzb021b_processed.png&w=3840&q=75)
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- The regression equation is: ŷ = 67.16 + 8.417x where ŷ is the miles traveled, and x is the MPG. The sample size used was all 110 MPG records. The correlation coefficient r = 0.620. Use the information to obtain an estimate of my mileage if my MPG is 22. Is it option: a.) cannot estimate ŷ rcrit = 0.195; the correlation IS NOT significant b.) ŷ = 252.33 rcrit = 0.195; the correlation IS significant c.) ŷ = 252.33 rcrit = 0.187; the correlation IS significant d.) cannot estimate ŷ rcrit = 0.187; the correlation IS NOT significantI need help with the test statistic only. Thanks.After you estimate a simple linear regression you obtain the following sample regression function: Y₁ = 8.8 +0.7091 Xį With an r² of 0.784. The observed dependent variable used for the regression is: Y 9 675 40 40 40 6 5 5 5 5 1 1 Compute the sample variance of X? 8.45 10.79 9.17 7.19 Cannot be computed with the provided information.
- A researcher would like to predici the dependent variable Y fram the two independent variables X, and Xg for a sample of N = 18 subjecis. Use muliple linear regression to calculate the coefficient of multiple determination and Lest the significance of the overall regression model. Use a significance level a = 0.01. Y 53.2 42.4 56.6 75 32.2 62.9 68.2 26.9 70.1 63 30.9 57,4 52 44.2 70.1 52.9 33 55.9 67.8 35.3 54.9 51.1 49 47.8 39.5 57.4| 46.9 45.7| 52.7 60.5 57.2 43.2 61.1 40.4 42 | 45,7 56.1 42.2 37.3 51.8 | 58.9 53,4 51.9 42.2 47.5 37.1 48 46.8 66 33.8 | 61.7 32.3 53.4 57.5 F= Pvalue - What is your decision for the hypothesis test? O Reject the null hypothesis, H.:B, = B =0 O Fail to reject H, What is your final conclusion? O The evidence supports the claim that one ar more of the regression coefficients is non-zero O The evidence supports the claim that all of the regression coelfficients are zero OThere is insufficient evidence to support the claim that at least one of the regression…J 1 The accompanying table shows results from regressions performed on data from a random sample of 21 cars. The response why variable is city fuel consumption in miles/gallons. The predictor X variables are weight weight in pounds, DISP engine displacement leaders NHW Y Highway fuel consumption in miles/gallons. It's only one predictor X variable is used to predict the city fuel consumption, which single variable is best? Why? The best variable is (WT/HWY/DISP) because it has the best combination of (large/small) p-value, (answer) and (large/small) adjusted R^2 (answer) (type integers are decimals. Do not round)The figure accompanying this exercise is a histogram of the residuals for a simple linear regression. Which condition(s) are verified in this figure? Be specific about which of the four necessary conditions are verified in this figure. (Select all that apply.) -2 -1 Residual O1. The form of the equation that links the mean value of y to x must be correct. 02. Observations in the sample are independent of each other. 03. For individuals in the population with the same particular value of x, the distribution of the values of y is a normal distribution with no outliers that influence the results unduly. 04. The standard deviation of the values of y from the mean y is the same regardless of the value of the x variable.
- The money raised and spent (both in millions of dollars) by all congressional campaigns for 8 recent 2-year periods are shown in the table. The equation of the regression line is y = 0.942x +27.609. Find the standard error of estimate s, and interpret the result. 793.9 1042.3 957.7 1203.3 450.7 673.7 745.1 778.6 Money raised, x Money spent, y 734.8 1024.2 929.1 1160.6 448.6 697.9 735.7 751.2 Find the standard error of estimate s, and interpret the result. (Round to three decimal places as needed.) How can the standard error of estimate be interpreted? O A. The standard error of estimate of the money raised for a specific amount of money spent is about s, million dollars. O B. The standard error of estimate of the money spent for a specific amount of money raised is about s, million dollars.27. What percentage of the variability is accounted for by the regression model?A. 1% B. 2% C. 98% D. 99%28. Is there a linear relationship between the dependent variable and the independent variables?A. Cannot be knownB. MaybeC. NoD. Yes29. What will be the value of y if x = 0?A. -12.87 B. -0.08 C. 0.70 D. 0.99An automobile rental company wants to predict the yearly maintenance expense (Y) for an automobile using the number of miles driven during the year () and the age of the car (, in years) at the beginning of the year. The company has gathered the data on 10 automobiles and run a regression analysis with the results shown below:. Summary measures Multiple R 0.9689 R-Square 0.9387 Adj R-Square 0.9212 StErr of Estimate 72.218 Regression coefficients Coefficient Std Err t-value p-value Constant 33.796 48.181 0.7014 0.5057 Miles Driven 0.0549 0.0191 2.8666 0.0241 Age of car 21.467 20.573 1.0434 0.3314 Use the information above to estimate the annual maintenance expense for a 10 years old car with 60,000 miles.
- please do this urgentlyA regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+b a=-0.666 b=36.053 r2=0.552049 r=-0.743 Use this to predict the number of situps a person who watches 4.5 hours of TV can do (to one decimal place)Calculate the co-efficient of correlation and the lines of regression for the following data: 1 2 3 4 5 7 8 9 X= y= 8 10 12 11 13 14 16 15. Obtain an estimate of y which should correspond on the average to x= 6. 2.
