please answer ty! (a) What percentage of observed variation in depression score change can be explained by the simple linear regression model?We wish to determine the percentage of variation in depression score change that can be explained by the simple linear regression model.First,recall the given Minitab output.Fitted Line PlotDepression score change = 6.826 + 4.802 BMI change15-:Ø10-5-0-0.00.51.0BMI changeDepression score change20-0.5S5.38140R-sq21.45%CoefficientsTerm Coef SE CoefConstant 6.8262.32BMI change 4.8022.911.5T-Value2.951.65SR-Sq21.45%R-Sq (adj) 13.59%ⓇP-Value0.0150.1295.38140Regression EquationDepression score change = 6.826+ 4.802 BMI changeVIF1.00Recall that the value describes the percentage of variation in y that can be explained by x in the simple linear regression model. According to the Minitab output, to two decimal places, ² =regression model, to two decimal places, is%.%, so the percentage of variation in depression score change that can be explained by the simple linear

It is given that Dependent variable Y : Depression score change. Independent variable X : BMI…

(a) What percentage of observed variation in depression score change can be explained by the simple linear regression model? We wish to determine the percentage of variation in depression score change that can be explained by the simple linear regression model.

(a) What percentage of observed variation in depression score change can be explained by the simple linear regression model? We wish to determine the percentage of variation in depression score change that can be explained by the simple linear regression model.

Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)

6th Edition

ISBN:9781337111348

Author:Bruce Crauder, Benny Evans, Alan Noell

Publisher:Bruce Crauder, Benny Evans, Alan Noell

Chapter3: Straight Lines And Linear Functions

Section3.CR: Chapter Review Exercises

Problem 15CR: Life Expectancy The following table shows the average life expectancy, in years, of a child born in...

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Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
Multiple regression is sometimes used in litigation. In the case of Cargill, Inc. v. Hardin (1971), the prosecution charged that the cash price of wheat was manipulated in violation of the Commodity Exchange Act. In a statistical study conducted for this case, a multiple regression model was constructed to predict the cash price of wheat using three supply-and-demand explanatory variables: economic growth, population growth, and meat consumption. Data for 24 years were used to construct the regression equation, and a prediction for the suspect period was computed from this equation. Which of the independent variables is the most significant predictor of the cash price of wheat? a. Intercept b. Economic Growth c. Population Growth d. Meat Consumption
Multiple regression is sometimes used in litigation. In the case of Cargill, Inc. v. Hardin (1971), the prosecution charged that the cash price of wheat was manipulated in violation of the Commodity Exchange Act. In a statistical study conducted for this case, a multiple regression model was constructed to predict the cash price of wheat using three supply-and-demand explanatory variables: economic growth, population growth, and meat consumption. Data for 24 years were used to construct the regression equation, and a prediction for the suspect period was computed from this equation Based on a significance level of 5%, which of the following independent variables significantly predict the cash price of wheat? a. Economic Growth b. Population Growth c. Meat Consumption d. All the independent variables significantly predict the cash price of wheat.
Multiple regression is sometimes used in litigation. In the case of Cargill, Inc. v. Hardin (1971), the prosecution charged that the cash price of wheat was manipulated in violation of the Commodity Exchange Act. In a statistical study conducted for this case, a multiple regression model was constructed to predict the cash price of wheat using three supply-and-demand explanatory variables: economic growth, population growth, and meat consumption. Data for 24 years were used to construct the regression equation, and a prediction for the suspect period was computed from this equation. The actual cash price of wheat under investigation in 1963 was $2.13. Based on the comparison of the correct predicted cash price calculated in the previous question and the actual cash price, what does the evidence suggest about Cargill, Inc.? a. Because the predicted price is relatively close to the actual price (within one cent), Cargill, Inc. probably did not artificially manipulate the price of wheat.…
Multiple regression is sometimes used in litigation. In the case of Cargill, Inc. v. Hardin (1971), the prosecution charged that the cash price of wheat was manipulated in violation of the Commodity Exchange Act. In a statistical study conducted for this case, a multiple regression model was constructed to predict the cash price of wheat using three supply-and-demand explanatory variables: economic growth, population growth, and meat consumption. Data for 24 years were used to construct the regression equation, and a prediction for the suspect period was computed from this equation. In 1963, during the period in question, economic growth was 3.8; population growth was 1.40; and meat consumption was 152.95. Based on these values, what would be the predicted cash price of wheat at this time in 1963?
Multiple regression is sometimes used in litigation. In the case of Cargill, Inc. v. Hardin (1971), the prosecution charged that the cash price of wheat was manipulated in violation of the Commodity Exchange Act. In a statistical study conducted for this case, a multiple regression model was constructed to predict the cash price of wheat using three supply-and-demand explanatory variables: economic growth, population growth, and meat consumption. Data for 24 years were used to construct the regression equation, and a prediction for the suspect period was computed from this equation. The following output represents the regression analysis. . Before the judge and jury consider the results of the regression model, they must ensure that the model is valid. What is the proper hypothesis test for this model, and what is the proper conclusion?
Which characteristic of a data set makes a linear regression model unreasonable? a slope close to 0 a correlation coefficient close to 0 a slope close to –1 a correlation coefficient close to –1
(a) Calculate the value of the sample correlation coefficient. (Round your answer to three decimal places.) ___________ (b) If you decided to fit the simple linear regression model to this data, what proportion of observed variation in maximum prevalence could be explained by the model relationship? (Round your answer to three decimal places.) ____________ (c) If you decided to regress UV transparency index on maximum prevalence (i.e., interchange the roles of x and y), what proportion of observed variation could be attributed to the model relationship? (Round your answer to three decimal places.) _____________ (d) Carry out a test of H0: ? = 0.5 versus Ha: ? > 0.5 using a significance level of 0.05. [Note: The article reported the P-value for testing H0: ? = 0 versus H0: ? ≠ 0.] (Round your test statistic to two decimal places and your P-value to four decimal places.) ___________
The owner of Showtime Movie Theaters, Inc. would like to predict weekly gross revenue as a function of advertising expenditures. Historical data for a sample of eight weeks follow. Weekly GrossRevenue($1000s) TelevisonAdvertising($1000s) NewspaperAdvertising($1000s) 96 5.0 1.5 90 2.0 2.0 95 4.0 1.5 92 2.5 2.5 95 3.0 3.3 94 3.5 2.3 94 2.5 4.2 94 3.0 2.5 Part A: Develop an estimated regression equation with the amount of television advertising as the independent variable. Part B: Develop an estimated regression equation with both television advertising and news paper advertising as independent variables. Part C: Is the estimated regression rquation coefficient for television advertising expenditures the same in part (a) and in part (b) ? Interpret the coefficient in each case. Part D : Predict Weekly gross revenue for a week $3500 is spent on television advertising and $1800 is spent on newspaper advertising? Please hurry
A researcher interested in explaining the level of foreign reserves for the country of Barbados estimated the following multiple regression model using yearly data spanning the period 2001 to 2016: ??=?+????+????+???I Where FR = yearly foreign reserves ($000’s), OIL = annual oil prices, EXP = yearly total exports ($000’s) and FDI = annual foreign direct investment ($000’s). The sample of data was processed using MINITAB and the following is an extract of the output obtained: Predictor Coef StDev t-ratio p-value Constant 5491.38 2508.81 2.1888 0.0491 Oil 85.39 18.46 4.626 0.0006 EXP -377.08 112.19 * 0.0057 FDI -396.99 160.66 -2.471 ** S = 2.45 R – sq = 96.3% R – sq (adj) = 95.3% Analysis of Variance Source DF SS MS F P Regression 3 1991.31 663.77 ? ?? Error 12 77.4 6.45 Total 15 e) Perform the F Test making sure to state…
A researcher interested in explaining the level of foreign reserves for the country of Barbados estimated the following multiple regression model using yearly data spanning the period 2001 to 2016: ??=?+????+????+???? Where FR = yearly foreign reserves ($000’s), OIL = annual oil prices, EXP = yearly total exports ($000’s) and FDI = annual foreign direct investment ($000’s). The sample of data was processed using MINITAB and the following is an extract of the output obtained: Predictor Coef StDev t-ratio p-value Constant 5491.38 2508.81 2.1888 0.0491OIL 85.39 18.46 4.626 0.0006EXP -377.08 112.19 * 0.0057FDI -396.99 160.66 -2.471 ** S = 2.45 R-sq = 96.3% R-sq(adj) = 95.3% Analysis of VarianceSource DF SS…
A researcher interested in explaining the level of foreign reserves for the country of Barbados estimated the following multiple regression model using yearly data spanning the period 2001 to 2016: ??=?+????+????+???? Where FR = yearly foreign reserves ($000’s), OIL = annual oil prices, EXP = yearly total exports ($000’s) and FDI = annual foreign direct investment ($000’s). The sample of data was processed using MINITAB and the following is an extract of the output obtained: Predictor Coef StDev t-ratio p-value Constant 5491.38 2508.81 2.1888 0.0491 OIL 85.39 18.46 4.626 0.0006 EXP -377.08 112.19 * 0.0057 FDI -396.99 160.66 -2.471 ** S = 2.45 R-sq = 96.3% R-sq(adj) = 95.3%…