Working with the Concepts19. Calorles and protein: The following table presents the numberof grams of protein and the number of calories per 100 gramsfor each of 18 fast-food products.ProteinCaloriesProteinCalories5.6236717.2513.732893763153.5818812.9621.2422.5416.2310.646.163.6911.972752682213092265.8440812.224608.051895.1536634416315.9316.52334241Source: United Stutes Department of Agriculturea. Compute the least-squares regression line for predictingcalories (y) frem protein (x).b. Construct a 95% confidence interval for theslope.c. Test H: p, = 0 versus H: 6+ 0. Can you conclude thatthe amount of protein is useful in predicting the number ofcalories? Use the a = 0.05 level of significance.20. Like father lil

19. a. The regression analysis is conducted using EXCEL. The software procedure is given below: Enter the data. Select Data > Data Analysis >Regression> OK. Enter Input Y Range as y. Enter Input X Range as x. Mark Labels. Click OK. The output using EXCEL is as follows: From the output, the intercept is 302.5212, and slope is -0.5082. The least-squares regression line for predicting calories (y) from protein (x) is, y^=a+bx=302.5212-0.5082x Thus, the least-squares regression line for predicting calories (y) from protein (x) is y^=302.5212-0.5082x.b. From the output, the 95% confidence interval for the slope is -7.8845,6.8680. Thus, the 95% confidence interval for the slope is (-7.8845,6.8680). c. From the output, the test statistic is -0.1461, and P-value is 0.8857. Decision rule: If P-value≤α, then reject the null hypothesis. Otherwise, do not reject the null hypothesis. Conclusion: The P-value (0.8857) is greater than the level of significance (0.05). Based on decision rule, fail to reject the null hypothesis. Thus, it cannot be conclude that the amount of protein is useful in predicting the number of calories.

Math

Statistics

a. Compute the least-squares regression line for predicting calories (y) frem protein (x). b. Construct a 95% confidence interval for the slope. C. Test H A=0 versus H P+ 0. Can you conclude that the amount of protein is useful in predicting the number of calories? Use the a = 0.05 level of significance.

a. Compute the least-squares regression line for predicting calories (y) frem protein (x). b. Construct a 95% confidence interval for the slope. C. Test H A=0 versus H P+ 0. Can you conclude that the amount of protein is useful in predicting the number of calories? Use the a = 0.05 level of significance.

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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Concept explainers

Correlation

Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.

Linear Correlation

A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.

Regression Analysis

Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.

Question

Working with the Concepts
19. Calorles and protein: The following table presents the number
of grams of protein and the number of calories per 100 grams
for each of 18 fast-food products.
Protein
Calories
Protein
Calories
5.62
367
17.25
13.73
289
376
315
3.58
188
12.96
21.24
22.54
16.23
10.64
6.16
3.69
11.97
275
268
221
309
226
5.84
408
12.22
460
8.05
189
5.15
366
344
163
15.93
16.52
334
241
Source: United Stutes Department of Agriculture
a. Compute the least-squares regression line for predicting
calories (y) frem protein (x).
b. Construct a 95% confidence interval for the
slope.
c. Test H: p, = 0 versus H: 6+ 0. Can you conclude that
the amount of protein is useful in predicting the number of
calories? Use the a = 0.05 level of significance.
20. Like father lil

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