Consider the following data where Age is the independent variable x (Expense is the dependent variable y). age 37 60 53 30 34 70 22 79 47 68 expenditure 15.74 34.14 22.76 13.71 14.24 31.57 7.63 42.26 23.67 41.64 a) Find the SSxx of the correlation coefficient. b) Find the SSyy of the correlation coefficient. c) Find the SSxy of the correlation coefficient. d) Find the value of b1 of the linear regression model y = b1x + b0. e) Find the value of b0 of the linear regression model y = b1x + b0. f) Find the estimated value in spending for a person with x = 86 years old using the linear regression model y = b1x + b0.
Added by Soledad G.
Close
Step 1
First, we calculate the mean of x: mean_x = (37 + 60 + 53 + 30 + 34 + 70 + 22 + 79 + 47 + 68) / 10 = 47 Show more…
Show all steps
Your feedback will help us improve your experience
Shyam P and 77 other Intro Stats / AP Statistics educators are ready to help you.
Ask a new question
Labs
Want to see this concept in action?
Explore this concept interactively to see how it behaves as you change inputs.
Key Concepts
Recommended Videos
If the correlation coefficient of the data above is 0.806, determine the linear relationship between the age and irrelevant answers. (Hint: use table II) Determine the coefficient of determination and interpret its meaning (write your answer in percent with one decimal place). The regression equation of the above data is y = -0.732x + 13.192. Interpret the slope of the regression line (Hint: b = -0.732). Using the regression equation, y = -0.732x + 13.192, predict the value of irrelevant answers when the age is 15 years.
Madhur L.
#2. (Multiple linear regression) Use the data given below to fit multiple linear regression of Expenditure as response variable (Y) Income, Age and WorkExp. as independent variables: Income (X1) $ | Expenditure (Y) $ | Age (X2) | WorkExperience (X3) in years 120 | 100 | 35 | 5.5 155 | 120 | 45 | 4.5 160 | 140 | 50 | 5.0 180 | 145 | 55 | 5.1 210 | 220 | 65 | 6.5 250 | 225 | 66 | 6.7 110 | 95 | 24 | 4.5 150 | 117 | 29 | 9.1 85 | 70 | 21 | 11.2 258 | 200 | 70 | 7.0 111 | 78 | 20 | 0.55 319 | 221 | 69 | 9.6 (a) What is the fitted model? (b) Estimate the expense of a person if his/her daily income is $ 280, age is 55 and work exp=7.2 years (c) Write five steps to test overall model is significant (F test) at alpha=5%. (d) Write five steps to test the hypothesis that income is significant on the model (t test) at alpha=5%. (e) Write five steps to test the hypothesis that age is significant on the model (t test) at alpha=5%.
Adi S.
Regression Analysis: BMI versus AGE Regression Equation: BMI = 18.367 + 0.282 * AGE Coefficients: - Constant: 18.367 - AGE: 0.2824 P-Values: - Constant: 0.039 - AGE: 0.0001 Model Summary: - R-Squared: 70.28% - Adjusted R-Squared: 71.13% - S: 1.82143 Analysis of Variance: - Regression: - DF: 1 - Adj SS: 277.92 - Adj MS: 277.918 - F-Value: 84.77 - P-Value: 0.0001 - Error: - DF: 35 - Adj SS: 112.80 - Adj MS: 3.318 Using this hypothesized output, we can write the results as follows: We used linear regression to assess the linear relationship. The R-square = %, which indicates that the model is . The model was significant F( , )= and p-value = . Then the P-value= for age, which means it is significant in BMI.
Recommended Textbooks
Elementary Statistics a Step by Step Approach
The Practice of Statistics for AP
Introductory Statistics
Watch the video solution with this free unlock.
EMAIL
PASSWORD