If we performed a t-test, how many people would be in the study based on the regression model in photo. The image contains two tables summarizing the results of a statistical analysis, possibly a linear regression model. Here is the transcription:**Table 1:** Model Summary- **R-Square:** 0.438- **Root MSE (Mean Square Error):** 6.78**Table 2:** Parameter Estimates- **Parameter**: Intercept - **Estimate**: 40.439 - **Standard Error**: 6.275 - **t Value**: 6.445 - **Pr > |t|**: 0.0000- **Parameter**: WEIGHT - **Estimate**: -0.521 - **Standard Error**: 0.164 - **t Value**: -3.182 - **Pr > |t|**: 0.0072**Explanation:**- **R-Square (0.438):** This indicates that approximately 43.8% of the variability in the response variable can be explained by the model.- **Root MSE (6.78):** This is the square root of the mean square error, indicating the standard deviation of the residuals, which shows how well the model predicts the data.- **Intercept:** The estimated value is 40.439, with a very small p-value (0.0000), indicating it is statistically significant.- **WEIGHT:** The coefficient estimate is -0.521, suggesting a negative association between the predictor and the response variable. The p-value (0.0072) indicates statistical significance.These results are typical outputs for a regression analysis, where the R-Square and Root MSE give an indication of overall model fit, and the parameter estimates provide specific insights into the variables' contributions.

Here simple linear regression output is given.The independent variable is weight.To find the number…

Answered: f we performed a t-test, how many…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

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Problem 1P

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The datasetBody.xlsgives the percent of weight made up of body fat for 100 men as well as other variables such as Age, Weight (lb), Height (in), and circumference (cm) measurements for the Neck, Chest, Abdomen, Ankle, Biceps, and Wrist. We are interested in predicting body fat based on abdomen circumference. Find the equation of the regression line relating to body fat and abdomen circumference. Make a scatter-plot with a regression line. What body fat percent does the line predict for a person with an abdomen circumference of 110 cm? One of the men in the study had an abdomen circumference of 92.4 cm and a body fat of 22.5 percent. Find the residual that corresponds to this observation. Bodyfat Abdomen 32.3 115.6 22.5 92.4 22 86 12.3 85.2 20.5 95.6 22.6 100 28.7 103.1 21.3 89.6 29.9 110.3 21.3 100.5 29.9 100.5 20.4 98.9 16.9 90.3 14.7 83.3 10.8 73.7 26.7 94.9 11.3 86.7 18.1 87.5 8.8 82.8 11.8 83.3 11 83.6 14.9 87 31.9 108.5 17.3…
Body Fat. Where we considered the regression of percentage of body fat on nine body measurements: height, weight, hip, forearm, neck, wrist, triceps, scapula, and sup. Describe and discuss problems that could have arisen in the collection of the data for this regression analysis.
Would the regression in Equation be useful for predicting test scoresin a school district in Massachusetts? Why or why not?
For a multiple regression analysis with 2 and 12 degrees of freedom, MS regression is 135 and MS residual is 15. What is the decision for this test?
An admissions counselor is looking to determine an equation that relates the graduate grade point averages of students who are newly admitted to an academic program with their undergraduate GPA, their GRE scores, and the number of years they have been out of college. What is the correct format for a multiple regression equation? Select the correct answer below: yˆ=b0+b1x1+b2x2+b3x3 yˆ=b1x1+b2x2+b3x3 yˆ=b0+b1x1+b2x2+b3x3+b4x4 yˆ=b0+b1x1+b2x2
Explain in your own words how simple regression can be helpful in identifying relationships between variables.
The U.S. Postal Service is attempting to reduce the number of complaints made by the public against its workers. To facilitate this task, a staff analyst for the service regresses the number of complaints lodged against an employee last year on the hourly wage of the employee for the year. The analyst ran a simple linear regression in SPSS. The results are shown below. The current minimum wage is $5.15. If an employee earns the minimum wage, how many complaints can that employee expect to receive? Is the regression coefficient statistically significant? How can you tell?
The accompanying table shows results from regressions performed on data from a random sample of 21 cars. The response (y) variable is CITY (fuel consumption in mi/gal). The predictor (x) variables are WT (weight in pounds), DISP (engine displacement in liters), and HWY (highway fuel consumption in mi/gal). Which regression equation is best for predicting city fuel consumption? Why? Click the icon to view the table of regression equations. Choose the correct answer below. A. The equation CITY=6.86 -0.00131WT -0.258DISP+0.659HWY is best because it has a low P-value and the highest value of R². B. The equation CITY=6.73 -0.00157WT +0.668HWY is best because it has a low P-value and the highest adjusted value of R². C. The equation CITY= -3.15+0.823HWY is best because it has a low P-value and its R² and adjusted R² values are comparable to the R² and adjusted R² values of equations with more predictor variables. O D. The equation CITY=6.86 -0.00131WT-0.258DISP + 0.659HWY is best because it…
A financial website reported the beta value for a certain company was 0.86. Betas for individual stocks are determined by simple linear regression. The dependent variable is the total return for the stock, and the independent variable is the total return for the stock market, such as the return of a market index. The slope of this regression equation is referred to as the stock's beta. Many financial analysts prefer to measure the risk of a stock by computing the stock's beta value. Suppose the following data show the monthly percentage returns for the market index and the company for a recent year. Month Market Index% Return Company% Return August -3 4 September 8 7 October 0 1 November -2 1 December -5 0 January 0 0 February 7 7 March 0 -2 April 2 0 May -5 -1 a. Develop the least squares estimated regression equation. (Let x = Market Index % Return (as a %), and let y = Company % Return (as a %). Round your numerical values to four decimal places.)
For the advertised price and number of miles data, the equation of the regression line is price = 18,773-86.18(thousands of miles) Predict the asking price for a car with 50,000 miles. $ type your answer...
Make a sample data about the factors that could be rated to the number of text/chat messages a senior high student/s sends in a day. Make a scenario out of it using the data needed in correlation and regression analysis. Use samples that are less than 30. Lastly, test your claim if it is true and form the regression equation for this scenario.
For 39 nations, a correlation of 0.887 was found between y = Internet use (%) and x = gross domestic product (GDP, in thousands of dollars per capita). The regression equation is y = -3.68 + 1.73x. Complete parts (a) through (c). a. Based on the correlation value, the slope had to be positive. Why? A. The slope and correlation are positive because gross domestic product could not be negative. B. The correlation and the slope are positive because the y-intercept is negative. C. That is a very unusual fact, because the slope and correlation usually have different signs. D. Although slope and correlation usually have different values, they always have the same sign.

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