A researcher would like to predict the dependent variable Y from the two independent variables X1 and X2 for a sample of N = 12 subjects. Use multiple linear regression to calculate the coefficient of multiple determination and test statistics to assess the significance of the regression model and partial slopes. Use a significance level a = 0.02. X1 X2 38.8 50.1 24.7 25.2 42.8 56.6 45.9 33.2 72.8 20.8 43.9 61.5 46.2 36.5 40.9 44.8 47.4 25.4 38.9 49 35.3 23.2 55 28.8 44.7 44.1 40 49.1 55.8 24.5 40.4 52.6 20 52.1 48.6 44.9 This data set can be downloaded as a *.csv file: Download CSV. R F = P-value for overall model = t1 for b1, P-value = t = for bz, P-value = What is your conclusion for the overall regression model (also called the omnibus test)? O The overall regression model is statistically significant at a = 0.02. O The overall regression model is not statistically significant at a = 0.02. Which of the regression coefficients are statistically different from zero? O neither regression coefficient is statistically significant O the slope for the first variable bị is the only statistically significant coefficient O the slope for the second variable bz is the only statistically significant coefficient

A researcher would like to predict the dependent variable Y from the two independent variables X1 and X2 for a sample of N = 12 subjects. Use multiple linear regression to calculate the coefficient of multiple determination and test statistics to assess the significance of the regression model and partial slopes. Use a significance level a = 0.02. X1 X2 38.8 50.1 24.7 25.2 42.8 56.6 45.9 33.2 72.8 20.8 43.9 61.5 46.2 36.5 40.9 44.8 47.4 25.4 38.9 49 35.3 23.2 55 28.8 44.7 44.1 40 49.1 55.8 24.5 40.4 52.6 20 52.1 48.6 44.9 This data set can be downloaded as a *.csv file: Download CSV. R F = P-value for overall model = t1 for b1, P-value = t = for bz, P-value = What is your conclusion for the overall regression model (also called the omnibus test)? O The overall regression model is statistically significant at a = 0.02. O The overall regression model is not statistically significant at a = 0.02. Which of the regression coefficients are statistically different from zero? O neither regression coefficient is statistically significant O the slope for the first variable bị is the only statistically significant coefficient O the slope for the second variable bz is the only statistically significant coefficient

A First Course in Probability (10th Edition)

10th Edition

ISBN:9780134753119

Author:Sheldon Ross

Publisher:Sheldon Ross

Chapter1: Combinatorial Analysis

Section: Chapter Questions

Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...

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A researcher would like to predict the dependent variable Y from the two independent variables X1 and X2 for a sample of N = 15 subjects. Use multiple linear regression to calculate the coefficient of multiple determination and test the significance of the overall regression model. Use a significance level a = 0.01. X1 X2 Y 38.4 58.8 48.2 63.4 47.9 55.6 48.4 54.6 33.1 54.9 48.7 47.4 54.8 54.2 51.7 44.1 78.6 64.8 55.3 48 38.6 56.8 54.3 34.1 56.5 61 42.6 52.1 49.2 35.8 57.5 49.4 51.2 55.3 66.7 56.7 57.5 53.5 25.6 71.8 64.4 63.3 29.1 62.8 56.7 Sreg SS.res R F P-value =
4. A study was conducted to investigate the relationship between the size of a house (in square feet) and the selling price of a house (in dollars). The response variable is price in dollars, and we want to study if the covariate of the square footage helps explain the response. A random sample of 522 houses was used, and the linear regression output from R is below. price sqft, data = house) 1m (formula = Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -81432.946 11551.846 -7.049 5.74e-12 *** sqft 158.950 4.875 32.605 <2e-16 *** Residual standard error: 79120 on 520 degrees of freedom Multiple R-squared: 0.6715, Adjusted R-squared: 0.6709 a. Write out the estimated linear equation. What is the estimated expected selling price of a house that is 2000 square feet? b. Does the intercept have a useful interpretation in this study? Why or why not. c. Interpret the slope estimate in context of the model.
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Find the regression equation, letting overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 2.3 cm. Can the prediction be correct? What is wrong with predicting the weight in this case? Use a significance level of 0.05. Overhead Width (cm) Weight (kg) 7.8 175 7.3 9.5 274 7.4 156 9.9 294 9.2 183 256 Click the icon to view the critical values of the Pearson correlation coefficient r. ..... The regression equation is y =+x. (Round to one decimal place as needed.)
A researcher would like to predict the dependent variable Y from the two independent variables X1 and X2 for a sample of N = 12 subjects. Use multiple linear regression to calculate the coefficient of multiple determination and test statistics to assess the significance of the regression model and partial slopes. Use a significance level a = 0.01. X1 X2 53.2 50.8 61.2 91.9 84.6 66.8 62 57.8 61.9 46.4 43 70.1 59.1 65.5 56.5 72.6 61.7 62.7 69.5 70.4 63.7 70.9 70.6 64.9 68.6 61.5 61.1 54.7 57 49 34.6 43.9 45 67.2 This data set can be downloaded as a *.csv file: Download CSV. 41.6 44.4 R F = P-value for overall model = ti = for b1, P-value = tą = for b2, P-value = What is your conclusion for the overall regression model (also called the omnibus test)? O The overall regression model is statistically significant at a = 0.01. O The overall regression model is not statistically significant at a = 0.01. Which of the regression coefficients are statistically different from zero? O neither…
On-base percentage plus slugging (OPS) is a statistic used in baseball to measure a team's batting success. The number of runs scored and OPS for 30 baseball teams was used to conduct a linear regression analysis. The scatterplot and computer output for the regression analysis is shown. 900- 850- 800- Number of 750- Runs Scored 700- 650- 600- 0.650 0.675 0.700 0.725 0.750 0.775 0.800 OPS Term Coef SE Coef Constant -838.40 77.99 OPS 2144.3 107.1 T-Value -10.75 20.01 P-Value <0.0001 < 0.0001 S = 19.516 R-Sq = 93.47% R-Sq(adj) 93.23% Which of the following is the most appropriate interpretation of the statistic 93.47% in the regression output? (A) There is a strong, positive, linear relationship between number of runs scored and OPS. (B) The typical deviation between observed and predicted number of runs scored is 0.9347. (C) For each one-unit increase in OPS, the regression model predicts an increase of 93.47 runs scored. (D) 93.47% of the observed number of runs scored are close to the…
Write the formula for the estimated regression line and interpret the slope of the estimated regression line, the intercept of the estimated regression line- Is it meaningful?, and the estimated R2. Based on the fitted regression model, what is the predicted ATST for a child who is 7 years old? What is the correlation between AGE and ATST? Does the residual plot suggest that the fitted regression line is inappropriate for these data? Explain why or why not. Suppose that a new subject is added to the study data and that subject is 12.5 years old with an ATST of 580 minutes. If the regression model were to be refit with this additional data point, would the new slope be greater than or less than -14.041? Justify your response.
Let x be the weight of a car (in hundreds of pounds), and let y be the miles per gallon (mpg). Suppose a car weighs 3800 pounds. What does the data forecast for the miles per gallon expected? x= 27 44 32 47 23 40 34 52 y= 30 19 24 13 29 17 21 14 State the correlation coefficient and state the regression lin equation.
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…
The fish in my pond have mean length 10 inches and mean weight 4 pounds. The correlation coefficient of length and weight is .9. If the length of a particular randomly selected fish is reported to be 10 inches, then what should we predict for the weight of that fish using simple linear regression? 4 10 2.5 NOT ENOUGH INFORMATION GIVEN NONE OF THE OTHERS
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Prev The table below gives the list price and the number of bids received for five randomly selected items sold through online auctions. Using this data, consider the equation of the regression line, y = bo + b₁x, for predicting the number of bids an Item will receive based on the list price. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, In practice, It would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Price in Dollars 28 33 36 42 45 Number of Bids 1 7 8 9 10 Step 3 of 6: Find the estimated value of y when x = 33. Round your answer to three decimal places. Table Copy Data Next