data
x | y |
48 | 102 |
42 | 107 |
47 | 107 |
43 | 102 |
44 | 115 |
42 | 101 |
55 | 87 |
57 | 91 |
56 | 97 |
59 | 82 |
57 | 78 |
54 | 95 |
43 | 102 |
44 | 115 |
42 | 101 |
excel
data -> data analysis -> regression
result
SUMMARY OUTPUT | |||||
Regression Statistics | |||||
Multiple R | 0.807968406 | ||||
R Square | 0.652812946 | ||||
Adjusted R Square | 0.626106249 | ||||
Standard Error | 6.602727568 | ||||
Observations | 15 | ||||
ANOVA | |||||
df | SS | MS | F | Significance F | |
Regression | 1 | 1065.651853 | 1065.651853 | 24.44379245 | 0.000268246 |
Residual | 13 | 566.7481473 | 43.59601133 | ||
Total | 14 | 1632.4 | |||
Coefficients | Standard Error | t Stat | P-value | Lower 95% | |
Intercept | 163.2969704 | 13.15625317 | 12.41211827 | 1.39236E-08 | 134.8746134 |
x | -1.319856146 | 0.26695761 | -4.944066388 | 0.000268246 | -1.896583 |
RESIDUAL OUTPUT | |||||
Observation | Predicted y | Residuals | |||
1 | 99.94387533 | 2.056124673 | |||
2 | 107.8630122 | -0.863012206 | |||
3 | 101.2637315 | 5.736268527 | |||
4 | 106.5431561 | -4.543156059 | |||
5 | 105.2232999 | 9.776700087 | |||
6 | 107.8630122 | -6.863012206 | |||
7 | 90.7048823 | -3.704882302 | |||
8 | 88.06517001 | 2.934829991 | |||
9 | 89.38502616 | 7.614973845 | |||
10 | 85.42545772 | -3.425457716 | |||
11 | 88.06517001 | -10.06517001 | |||
12 | 92.02473845 | 2.975261552 | |||
13 | 106.5431561 | -4.543156059 | |||
14 | 105.2232999 | 9.776700087 | |||
15 | 107.8630122 | -6.863012206 |
a) this is the slope = -1.3199
b)
x = 45
y^= 163.2970 -1.3199* x
= 163.2970 -1.3199* 45
= 103.9015
c) residual for 8th case
= 2.9348
d) point estimate for error variance = MSE = 43.596
Using the data above I have to answer the questions below It’s a decreasing relationship between...
SPSS only to get the p A / 48 / 42 / 47 / 43 | 44 | 42 Iss | 57| s6 | 59|57 | S4| 43 44 42 M.M 102 107 107 102 115 101 7 9197 82 78 95 102 115 101 A = Age, M.M. Muscle Mass bu hand Spss Plot the estimated regression function and the data. Does a linear regression function appear to give a good fit here? Does your plot support the anticipation...
Problem 8.4: Refer to Muscle Mass Problem 1.27. Second-order regression model (8.2) with independent normal error terms is expected to be appropriate. A. Fit regression model (8.2). Plot the fitted regression function and the data. Does the quadratic regression function appear to be a good fit here? Find R^2. B. Test whether or not there is regression relation; use α= .05. State the alternatives, decision rule and conclusion. C. Estimate the mean muscle mass for women aged 48...
The data from data95.dat contains information on 78 seventh-grade students. We want to know how well each of IQ score and self-concept score predicts GPA using least-squares regression. We also want to know which of these explanatory variables predicts GPA better. Give numerical measures that answer these questions. (Round your answers to three decimal places.) (Regressor: IQ) R 2 : (Regressor: Self-Concept) R 2 : Which variable is the better predictor? IQSelf Concept obs gpa iq gender concept 1 7.94...
Use the Tornadoes Data and your statistical expertise to answer the questions: Is it reasonable to claim that the average number of observed tornadoes per year is different from the average number of tornado related deaths per year? 5. What test/procedure did you perform? a. One-sided t-test b. Two-sided t-test c. Regression d. Confidence interval 6. What is the P-Value/margin of error? a. 0.007034504 b. 0.34922 c. 4.07497E-24 d. 2.03749E-24 e. None of these 7. Statistical Interpretation a. Since P-value...
We are interested in the relationship between the compensation of Chief Executive Officers (CEO) of firms and the return on equity of their respective firm, using the dataset below. The variable salary shows the annual salary of a CEO in thousands of dollars, so that y = 150 indicates a salary of $150,000. Similarly, the variable ROE represents the average return on equity (ROE)for the CEO’s firm for the previous three years. A ROE of 20 indicates an average return...
We assume that our wages will increase as we gain experience and become more valuable to our employers. Wages also increase because of inflation. By examining a sample of employees at a given point in time, we can look at part of the picture. How does length of service (LOS) relate to wages? The data here (data238.dat) is the LOS in months and wages for 60 women who work in Indiana banks. Wages are yearly total income divided by the...
Need some assistance of
reorganizing this whole program. I have the right code for
everything I just need help on putting all the codes in the right
spot so it can come out to the correct output.
output is supposed to look like this:
1 \\ user inputs choice to convert 12 to 24
8 \\ user inputs 8 for hours
30 \\ user inputs 30 for minutes
20 \\ user inputs 20 for seconds
AM \\ user inputs AM...
2. Using the data set of the Health Exam Results, conduct the following analysis between the variables of weight (WT) and Body Mass Index (BMI). Number the data set from 1 to 40, and select the following individuals: . Set 1 (Malo): 1, 5, 10, 13, 15, 18, 19, 24, 29, 31, 32, 33 .Set 2 (Fomalo): 4, 9, 15, 16, 17, 22, 23, 29, 33, 37, 38, 40 Draw a scatter diagram of the sample of 12 data set...
so i have my c++ code and ive been working on this for hours
but i cant get it to run im not allowed to use arrays. im not sure
how to fix it thank you for the help
our job is to write a menu driven program that can convert to display Morse Code ere is the menu the program should display Menu Alphabet Initials N-Numbers - Punctuations S = User Sentence Q- Quit Enter command the user chooses...
I have written my code for an employee management system that stores Employee class objects into a vector, I am getting no errors until I try and compile, I am getting the error: C2679 binary '==': no operator found which takes a right-hand operand of type 'const std::string' (or there is no acceptable conversion). But I am not sure why any help would be great, Thank you! 1 2 3 4 5 6 7 8 9 10 11 12 13...