Suppose that we performed an estimate of the determinants of players' salaries forthe 2018 NFL season. The R? = 0.65 for the regression that included 3 explanatoryvariables. Imagine that we decided to add an additional explanatory variable (like theplayers' previous playoff experience). When we run the regression with theadditional explanatory variable the R2 = 0.78 is for the regression. We can interpretthis asthe additional explanatory variable is significant at the 5% level.the additional explanatory variable has lowered the explained sum of squaresmeasure in the regression.the additional explanatory variable is related to player salary and does belong inthe regression.the additional explanatory variable is not related to player salary and does notbelong in the regression.

Given information: The coefficient of determination for the old model is R1 2 = 0.65. The…

Answered: Suppose that we performed an estimate…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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The table below gives the age and bone density for 5 women. Use the equation of the regression line, y= b0 + b1x, for predicting a women's bone density based on her age. The correlation coefficient may or may not be statically significant for the data given. Remember it wouldn't be appropiate to use regression line to make a prediction if the correlation coefficient isn;t statically significant. (y has a "hat" on the top) age 39 51 54 56 67 bone density 355 349 347 315 313 Find the estimated slope. Rund your answer to three decimal places. Find the estimated y-intercept. Round your answer to three decimal places. Determine the value of the dependent variable y at x+ 0 (y has a "hat" onthe top) Find the estimated value of y when x = 51. Round your answer to three decimal places. Substitute the values you found in steps 1 and 2 into the equation for the regression line to find the estimated linear model. According to this model, if the valueof the…
You may need to use the appropriate technology to answer this question. A regression analysis involving 45 observations relating a dependent variable and two independent variables resulted in the following information. ý = 0.406 + 1.3385X, + 2X2 The SSE for the above model is 43. When two other independent variables were added to the model, the following information was provided. ý = 1.9 – 3X + 12X2 + 4Xg + 8x, This model's SSE is 36. At a 0.05 level of significance, test to determine if the two added independent variables contribute significantly to the model. State the relevant null and alternative hypotheses. O Ho: One or more of the parameters is not equal to zero. H₂: B₁ = B₂= B3 =B4 = 0 O Ho: One or more of the parameters is not equal to zero. H₂: B3 =B4 = 0 O Ho: B3 = P4 = 0 H₂: None of the parameters are equal to zero. ⒸH₁: B3 =B₁ = 0 H: One or more of the parameters is not equal to zero. O Ho: B₁ = P₂ = B3 =B4 = 0 H: One or more of the parameters is not equal to zero. ✔ Find…
How does R treat those observations with missing values? In other words, what role do the observations with missing values play in the regression? No command is needed for this question. You must need to provide an answer or take a guess.
when a regression is used as a method of predicting dependent variables from one or more independent variables. How are the independent variables different from each other yet related to the dependent variable?
An e-commerce retailer was trying to understand how customer engagement correlates to customer purchases. They built the following regression model: Probability of Purchase = 0.1619 + 0.07 Emails Opened. If someone opens 5 emails how likely are they to purchase? If someone has a 75% chance of purchase how many emails have they opened? If someone opens no emails how likely are they to purchase? What is the slope here and what does it tell us?
A group of researchers measured how funny people rated a cartoon when they were in one of two groups: holding a pen in their teeth (forcing them to smile) or holding a pen in their lips (forcing them to frown). For this study, identify the independent variable and the dependent variable. Justify your response.
Range of ankle motion is a contributing factor to falls among the elderly. Suppose a team of researchers is studying how compression hosiery, typical shoes, and medical shoes affect range of ankle motion. In particular, note the variables Barefoot and Footwear2. Barefoot represents a subject's range of ankle motion (in degrees) while barefoot, and Footwear2 represents their range of ankle motion (in degrees) while wearing medical shoes. Use this data and your preferred software to calculate the equation of the least-squares linear regression line to predict a subject's range of ankle motion while wearing medical shoes, ?̂ , based on their range of ankle motion while barefoot, ? . Round your coefficients to two decimal places of precision. ?̂ = A physical therapist determines that her patient Jan has a range of ankle motion of 7.26°7.26° while barefoot. Predict Jan's range of ankle motion while wearing medical shoes, ?̂ . Round your answer to two decimal places. ?̂ = Suppose Jan's…
If you have 2 datasets on which you have conducted a regression analysis, how would you determine whether you can combine them into one dataset?
What are the coefficients of correlation between miles per gallon and horsepower? Between miles per gallon and the weight of the car? What are the directions and strengths of these coefficients? Do the coefficients of correlation indicate a strong correlation, weak correlation, or no correlation between these variables? See Step 4 in the Python script. Write the multiple regression equation for miles per gallon as the response variable. Use weight and horsepower as predictor variables. See Step 5 in the Python script. How might the car rental company use this model?
You decide to add a second independent variable to your regression. Male is a dummy variable that equals 1 if the individual is male and 0 otherwise. Your regression results are now: wage = 3.071 + 0.289 × Years of Schooling + 1.28 × Male (0.143) (0.0168) (0.624) Interpret the coefficients from this regression. Make sure to clearly indicate what is changing and what is constant in each interpretation. Determine whether each coefficient is statistically significant at each of the conventional significance levels. The R2 for this regression is 0.316. Interpret the meaning of this value.
A negative correlation means that decreases in the X variable tend to be accompanied by decreases in the Y variable. T or F
PLEASE ANSWER 4,5 AND 6

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