Seventy-six Starbucks food items were analyzed for the calorie and carbohydrate content. We used linear regression to explore the relationship between the number of calories and amount of carbohydrates (in grams) Starbucks food menu items contain. The estimated regression equation with carbohydrates as the response variable and the calories as the explanatory variable is ŷ = 8.94 + 0.11x, and summary statistics of the two variables is provided below. variable min Q3 Q1 median sd n missing max mean calories 80 300 350 420 500 338.8 105.4 77 carbohydrates 16 31 45 59 80 44.9 16.6 77

Seventy-six Starbucks food items were analyzed for the calorie and carbohydrate content. We used linear regression to explore the relationship between the number of calories and amount of carbohydrates (in grams) Starbucks food menu items contain. The estimated regression equation with carbohydrates as the response variable and the calories as the explanatory variable is ŷ = 8.94 + 0.11x, and summary statistics of the two variables is provided below. variable min Q3 Q1 median sd n missing max mean calories 80 300 350 420 500 338.8 105.4 77 carbohydrates 16 31 45 59 80 44.9 16.6 77

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

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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

17. What is the interpretation of the intercept?
a. 8.94% of the variability in carbohydrates can be explained by calories.
b. Food items with zero calories are estimated to have 8.94 grams of carbohydrates.
c. Food items with 0.11 calories are estimated to have 8.94 grams of carbohydrates.
d. Food items with 338.8 calories are estimated to have 8.94 grams of carbohydrates.
e. Food items with 0 grams of carbohydrates are estimated to have 8.94 calories.

Seventy-six Starbucks food items were analyzed for the calorie and carbohydrate content. We
used linear regression to explore the relationship between the number of calories and amount
of carbohydrates (in grams) Starbucks food menu items contain. The estimated regression
equation with carbohydrates as the response variable and the calories as the explanatory
variable is ŷ = 8.94 + 0.11x, and summary statistics of the two variables is provided below.
variable min
Q1 median
Q3
max
mean
sd
n missing
calories
80 300
350 420
500
338.8
105.4 77
carbohydrates
16
31
45
59
80
44.9
16.6 77

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