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Make a linear model for the following data
(1,13) (5,20) (9,27) (13,34)
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- The following table was generated from the sample data of 10 junior high students regarding the average number of hours they are unsupervised per night, the average number of hours they play video games per night, and their final grades in their math class. The dependent variable is the final grade, the first independent variable (x1) is the number of hours unsupervised each night, and the second independent variable (x2) is the number of hours of video games each night. Coefficients Standard Error t-Stat p-value Intercept 62.711817 4.607133 13.611896 0.000010 Hours Unsupervised 0.635048 0.915495 0.693666 0.513836 Hours Playing Video Games 7.528738 1.296727 5.805955 0.001145 Copy Data Step 1 of 2: Write the multiple regression equation for the computer output given. Round your answers to three decimal places. Answer 田 Tables 国 Keypad How to enter your answer (opens in new window) Keyboard Shortcuts X +A university would like to examine the linear relationship between a faculty member's performance rating (measured on a scale of 1-20) and his or her annual salary increase. The table to the right shows these data for eight randomly selected faculty members. Complete parts a and b.8. Find the line of best fit y = a + bx for the data points (1,3), (2,4) and (-1,-1).
- Q9C Levi-Strauss Co manufactures clothing. The quality control department measures weekly values of different suppliers for the percentage difference of waste between the layout on the computer and the actual waste when the clothing is made (called run-up). The data is in the following table, and there are some negative values because sometimes the supplier is able to layout the pattern better than the computer ("Waste run up," 2013). Table #11.3.3: Run-ups for Different Plants Making Levi Strauss Clothing Plant 1 Plant 2 Plant 3 Plant 4 Plant 5 1.2 16.4 12.1 11.5 24 10.1 -6 9.7 10.2 -3.7 -2 -11.6 7.4 3.8 8.2 1.5 -1.3 -2.1 8.3 9.2 -3 4 10.1 6.6 -9.3 -0.7 17 4.7 10.2 8 3.2 3.8 4.6 8.8 15.8 2.7 4.3 3.9 2.7 22.3 -3.2 10.4 3.6 5.1 3.1 -1.7 4.2 9.6 11.2 16.8 2.4 8.5 9.8 5.9 11.3 0.3 6.3 6.5 13 12.3 3.5 9 5.7 6.8 16.9…The following table was generated from the sample data of 1010 junior high students regarding the average number of hours they are unsupervised per night, the average number of hours they play video games per night, and their final grades in their math class. The dependent variable is the final grade, the first independent variable (x1x1) is the number of hours unsupervised each night, and the second independent variable (x2x2) is the number of hours of video games each night. Coefficients Standard Error t-Stat p-value Intercept 57.968435 7.227955 8.020032 0.000201 Hours Unsupervised 5.131630 1.314283 3.904510 0.007943 Hours Playing Video Games 1.067855 1.615039 0.661194 0.533040 Indicate if any of the independent variables could be eliminated at theThe basic hypothesis is that people who play sports are more likely than others to watch sport on TV. Your first task is to crosstabulate the data involving the playing and watching of sports and to determine the direction and strength of that relationship. Write out your findings about this basic relationship. Your next task is to determine the direction and strength of the partial relationships when you control for the gender of individuals.
- Consider the following data set x: 1 2 3 4 5 y: 4 4.4 5.2 6.4 8 Fit it to a linear model and a quadratic model. Which model is better, and how do you know?Consider the following population model for household consumption: cons = a + b1 * inc+ b2 * educ+ b3 * hhsize + u where cons is consumption, inc is income, educ is the education level of household head, hhsize is the size of a household. Suppose that the variable for consumption is measured with error, so conss = cons + e, where conss is the mismeaured variable, cons is the true variable, e is random, i.e., e is independent of all the regressors. What would we expect and why? A) OLS estimators for the coefficients will all be biased B) OLS estimators for the coefficients will all be unbiased C) ALL the standard errors will be bigger than they would be without the measurement error D) both B and CA random sample of 136 adults were asked to report the number of hours per week the spent on a computer and their number of years of education. The linear model equation below describes the relationship between the mean computers hours and years of education. computers == 9.12 ++ 0.8 ×× education Based on this linear model, which of the following statements is correct? a) An adult who has no years of education is expected to spend 9.12 hours per week on a computer. b) An adult who has 1 more year of education than another is expected to spend 9.12 more hours per week on a computer. c) An adult who spends 1 more hour per week on a computer than another is expected to have had 9.12 more years of education. d) An adult who spend zero hours per week on a computer is expected to have 9.12 years of education.
- This dataset of size n - 51 is for the 50 states and the District of Columbia in the United States. The variables are - year 2002 birth rate per 1000 females 15 to 17 years old and z- poverty rate (the percent of the state's population living in households with in- comes below the federally defined poverty level) i- 13.1 - 22.3 R- 51, Su - E(-2) - 914.7 S, - E( -2)( - 6) - 1, 256.2 Su - E(m - M) - 3, 249 SSE - E(w - - 1, 509.6 1. Find A. A and r. 2. Write the fitted linear regression model for ý, in terms of z, A, and A- For A and , use the values you obtained in problem 1. 3. Interpret the slope of the least squares line based on the context. 4. Find SST, SE and SSR. 5. Find R* and . 6. Complete a chart for ANOVA df MS pvalue Source Regression Error Total 7. Complete a chart for Parameter Estimates. t-value pvalue Parameter Intercept Slope Estimate S.e. 8. What is the residual e, for the observation (.) - (20.1,31.5)? 9. What is the conclusion for the test Ha : -0 vs Ho : +0 with a -…The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. xx 11.4 8 7 3.6 2.7 2.5 2.7 0.8 yy 13.4 11 10.1 7.2 6.2 6.1 6.4 4.7 xx = thousands of automatic weaponsyy = murders per 100,000 residentsThis data can be modeled by the equation y=0.84x+4.09.y=0.84x+4.09. Use this equation to answer the following;A) How many murders per 100,000 residents can be expected in a state with 4.7 thousand automatic weapons?Answer = Round to 3 decimal places.B) How many murders per 100,000 residents can be expected in a state with 6.6 thousand automatic weapons?Answer = Round to 3 decimal places.This data table contians the listed prices and weights of the diamonds in 48 rings offered for sale in The Singapore Times. The prices are in Singapore dollars, with the weights in crats. Estimate the linear regression using weight as the explanatory variable and price as the response variable using the following two methods: a) Create a scatterplot with trendline (be sure to show the equation and R-square on the chart.) b) Use the Data Analysis Toolpak to do the work for you.