Player Distance yards Accuracy% 316.9 304.6 310.8 312.8 52.1 294.9 64.3 291.8 63.8 295.5 62.2 8 310.7 53.5 Fit the model to the data using simple linear regression and give the least squares prediction equation. 1 2 3 4 5 6 7 48.4 57.9 55.9
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- Life Expectancy The following table shows the average life expectancy, in years, of a child born in the given year42 Life expectancy 2005 77.6 2007 78.1 2009 78.5 2011 78.7 2013 78.8 a. Find the equation of the regression line, and explain the meaning of its slope. b. Plot the data points and the regression line. c. Explain in practical terms the meaning of the slope of the regression line. d. Based on the trend of the regression line, what do you predict as the life expectancy of a child born in 2019? e. Based on the trend of the regression line, what do you predict as the life expectancy of a child born in 1580?2300The 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 shown below. Table 7: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .854a .730 .695 6.6235 a. Predictors: (Constant), Hourly Wage Table 8: ANOVA ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 1918.458 1 1918.458 129.783 .000a Residual 709.567 48 14.782 Total 2628.025 49 a. Predictors: (Constant), Hourly Wage b. Dependent Variable: Number of Complaints Table 9: Coefficients Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 20.2 4.357 4.636 .000 Hourly Wage -1.20 .088 -.946 -13.636 .000 a. Dependent Variable: Number of…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 shown below. Table 7: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .854a .730 .695 6.6235 a. Predictors: (Constant), Hourly Wage Table 8: ANOVA ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 1918.458 1 1918.458 129.783 .000a Residual 709.567 48 14.782 Total 2628.025 49 a. Predictors: (Constant), Hourly Wage b. Dependent Variable: Number of Complaints Table 9: Coefficients Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 20.2 4.357 4.636 .000 Hourly Wage -1.20 .088 -.946 -13.636 .000 a. Dependent Variable: Number of…
- Draw a scatter diagram of the data, treating age as the explanatory variable. What type of relation, if any, appears to exist between age and HDL cholesterol? Determine the least-squares regression equation from the sample data. Y=McDonalds Corporation has reported the following values for total revenues and net income during the 1998 to 2005 period. All data (in photo attached) are in billions of dollars: Source: McDonalds Corporation, 2005 Annual Report. (a) Determine the least-squares regression equation line for estimating net income and interpret its slope. (b) For a year in which total revenues are $18.0 billion, estimate the net income for that year. (c) Also find a 95% Confidence and Prediction Interval for the above estimate. (d) Find the sample correlation coefficient for the model.The outcome variable in a multiple linear regression is best measured on which of these scales? categorical quantitative ordinal all of the above
- A grocery store owner wants to know if rainfall is positively related to how much money his store earns in a given day. For a month, compares the number of inches it rains each day to the amount of money his store makes each day. What type of statistical analysis would you use to answer this question? Single-sample t-test Dependent Samples t-test Independent Samples t-test One-way ANOVA Bivariate Correlation Bivariate Linear RegressionNeed help with: Draw a scatter diagram of the data, treating age as the explanatory variable. What type of relation, if any, appears to exist between age and HDL cholesterol? Determine the least-squares regression equation from the sample data. Y= Are there any outliers or influential observations? Use technology to compute the P-value. Use the Tech Help button for further assistance What conclusion can be drawn at α=0.01 level of significance? Assuming the residuals are normally distributed, construct a 95% confidence interval about the slope of the true least-squares regression line. What is upper bound= & lower bound= For a 42-year-old male patient who visits the doctor's office, would using the least-squares regression line obtained in part (b) to predict the HDL cholesterol of this patient be recommended? A good estimate for the HDL cholesterol of this patient is(e) Find the least-squares regression line treating square footage as the explanatory variable.
- Develop a scatter plot between annual household spending and annual income. The scatter diagram should include a proper title, a solid trend line, axis titles, and a regression equation, and the Coefficient of Determination (R –square). Monthly Household Spending ($) Annual Household Income ($) HouseholdSize 4,016 54000 3 3,159 30000 2 5,100 32000 4 4,742 50000 5 1,864 31000 2 4,070 55000 2 2,731 37000 1 3,348 40000 2 4,764 66000 4 4,110 51000 3 4,208 25000 3 4,219 48000 4 2,477 27000 1 2,514 33000 2 4,214 65000 3 4,965 63000 4 4,412 42000 6 2,448 21000 2 2,995 44000 1 4,171 37000 5 5,678 62000 6 3,623 21000 3 5,301 55000 7 3,020 42000 2 4,828 41000 7 5,573 54000 6 2,583 30000 1 3,866 48000 2 3,586 34000 5 5,037 67000 4 3,605 50000 2 5,345 67000 5 5,370 55000 6 3,890 52000 2 4,705 62000 3 4,157 64000 2 3,579 22000 3 3,890 29000 4 2,972 39000 2 3,121 35000 1 4,183 39000 4 3,730 54000 3 4,127…Draw a scatter diagram for the above data Obtain the regression equation by using least square method Predict the price of a house with a size of 28 square feetThe head width (in) and weight (lb) is measured for a random sample of 20 bears.The data shows that the mean head width is 6.9 inches, mean weight is 214.3 lb, and thecorrelation r = 0.879 and its p-value is less than 0.0001. The suggested linear regressionequation is WEIGHT = -212 + 61.9 WIDTH.(a) How is the best predicted weight value of a given head width found with this data found?(b) For the preceding part, why?(c) Find the best predicted weight given a bear with a head with of 6.5 inches.
