### Ice Cream and Soda Sales Correlation StudyBeverly conducted a study this past spring using data collected from a cafeteria. She recorded weekly data on ice cream sales and soda sales. The diagram below includes the line of best fit and the correlation coefficient derived from the study.#### Beverly's Cafeteria Study![Graph showing a correlation between ice cream bars sold and cans of soda sold](image-link)**Graph Explained:**- The horizontal axis represents the number of ice cream bars sold.- The vertical axis represents the number of cans of soda sold.- Each point on the graph represents a data pair of ice cream bars sold and soda cans sold for a particular week.- The line of best fit, shown on the graph, suggests a positive correlation between the two variables.- The correlation coefficient (r) is 0.96, indicating a very strong positive correlation.#### Interpretation of ResultsGiven the information provided, which statements can be correctly concluded?1. I. Eating more ice cream causes a person to become thirsty.2. II. Drinking more soda causes a person to become hungry.3. III. There is a strong correlation between ice cream sales and soda sales.**Answer Choices:**1. I, only2. III, only3. I and III4. II and III**Correct Answer:**- 2. III, only### Understanding the ResultsThe data from the study demonstrate a strong correlation between ice cream sales and soda sales in the cafeteria. However, causation cannot be inferred from correlation. Thus, while statement III is valid, statements I and II are not supported by the study's data.**Note:**Correlation coefficient values range from -1 to 1. Values approaching 1 or -1 suggest a strong relationship, while values near 0 indicate a weak or no relationship. In this study, the correlation coefficient of 0.96 suggests a very strong positive correlation, meaning that as ice cream sales increase, soda sales tend to increase as well.

In the following question, the data collector has the correction coefficient of 0.96 then,

Beverly did a study this past spring using data she collected from a cafeteria. She recorded data weekly for ice cream sales and soda sales. Beverly found the line of best fit and the correlation coefficient, as shown in the diagram below. Beverly's Cafeteria Study r3.96 Ice Cream Bars Sold Given this information, which statement(s) can correctly be concluded? I. Eating more ice cream causes a person to become thirsty. II. Drinking more soda causes a person to become hungry. III. There is a strong correlation between ice cream sales and soda sales. 1. I, only 2. III, only 3. I and II 4. Il and III Cans of Soda Sold

Beverly did a study this past spring using data she collected from a cafeteria. She recorded data weekly for ice cream sales and soda sales. Beverly found the line of best fit and the correlation coefficient, as shown in the diagram below. Beverly's Cafeteria Study r3.96 Ice Cream Bars Sold Given this information, which statement(s) can correctly be concluded? I. Eating more ice cream causes a person to become thirsty. II. Drinking more soda causes a person to become hungry. III. There is a strong correlation between ice cream sales and soda sales. 1. I, only 2. III, only 3. I and II 4. Il and III Cans of Soda Sold

Algebra and Trigonometry (6th Edition)

6th Edition

ISBN:9780134463216

Author:Robert F. Blitzer

Publisher:Robert F. Blitzer

ChapterP: Prerequisites: Fundamental Concepts Of Algebra

Section: Chapter Questions

Problem 1MCCP: In Exercises 1-25, simplify the given expression or perform the indicated operation (and simplify,...

See similar textbooks

Similar questions

For the following pairs of variables, indicate whether you would expect a positive correlation, a negative correlation, or close to 0 correlation. Maximum daily temperature and cooling costs Interest rates and number of loans Incomes of husbands and wives in dual career families Height and IQ Height and shoe size Score on the Math SAT and Verbal SAT scores Time spent on video games and college GPA Use of fertilizer and yield of crops
Answer quickly please. Need answer ASAP. Choose one letter only. No need for long explanation. 1. A characteristic of interest for the elements is called: A. a sample B. a data set C. a variable D. an observation 2. What does the correlation coefficient (r) measure in a correlation analysis? A. The strength and direction of a linear relationship between two variables. B. The causal relationship between two variables. C. The difference between the means of the two groups. D. The variability of a single variable. 3. You work as an analyst for a retail company, and you are interested in understanding how advertising spending (independent variable) affects monthly retail sales (dependent variable). In a simple linear regression equation, what does the regression slope coefficient (B1) represent in this scenario? A. The average monthly retail sales for all companies B. The change in average monthly retail sales is associated with a one-unit increase in advertising spending. C. The…
Suppose a researcher is interested in examining the relationship between a person 's age and whether he or she likes the taste of cilantro. She collects a sample of n- 10 people and asks them whether they like the taste of cilantro. The following table summarizes the results. Does not like Cilantro Does like Cilantro 18 years old 2 18+ years old 3 The researcher wants to calculate the correlation between a person 's age and whether he or she likes the taste of cilantro. To do so, you (the researcher) must first create a table of the data by converting each variable to a numerical value. Assign a 0 to "< 18 years old" and 1 to "18+ years old." Then assign O to "does not like the taste of cilantro" and a 1 to " likes the taste of cilantro." Complete the top two rows of the your (the researcher's) data. Age (0 is <18; 1 is 18+) Likes the taste of Cilantro (0 is no; 1 is yes) a) ob) 1 a) Ob) 1 a) ob) 1 a) Ob) 1 1 1 1 1 1 1 1 The phi-coefficient is a) -0.09 b) -0.16 c) -0.20 d) 0.80
Susan, a personal trainer, was interested in whether or not there was a linear relationship between the number of visits her clients made to the gym each week and the average amount of time her clients exercised per visit. She took the following data. Client 1 2 3 4 5 6 Number of visits per week 1 3 4 2 3 5 Average time spent exercising per visit (hours) 2 1.5 1 2 2 0.30 If the correlation coefficient is –1, which answer is correct? Group of answer choices The slope of the best fit line is positive. The data fit exactly on a line with negative slope. The slope of the best fit line is –1. The data fit exactly on a line with positive slope.
For a data set of weights (pounds) and highway fuel consumption amounts (mpg) of twelve types of automobile, the linear correlation coefficient is found and the P-value is 0.009. Write a statement that interprets the P-value and includes a conclusion about linear correlation.
Please help solve.
Statistics Question
2. Consider the following examples; would you expect the correlation coefficient to be close to 1, -1, or 0 (10 points): The amount of time a dog is walked, and the number of hours the dog sleeps at night Outdoor temperature and snowfall. The length of time someone wears braces and their SAT scores. 3. The following graph represents the amount of time that someone studies for their final exam, and their final examination grade (HINT) (20 points). Student 1 Student 2 Student 3 Student 4 Student 5 Student 6 Hours Studied 4 7 11 8 10 8 Final Exam Grade 55% 72% 88% 93% 91% 84% Graph the following students as ordered pairs. Let the x-axis represent the hours studied, and the y-axis represent the students’ final exam grade. Circle the value that best represents the estimate of r: (circle one) -0.99, -0.62, -0.30, 0, 0.30, 0.75, 0.85, 0.99
Jim and Larry run a soup stand. The data below represents the sales of soup that they had on 8 randomly selected days, along with the high temperature on that day. a) Create a scatter diagram for this data set and comment on whether there is a positive or negative relationship between these variables.b) Calculate the correlation coefficient between temperature and sales.c) Does a linear relationship exist between these variables? Explain to me why or why not this is the case.d) Using excel, calculate the least squares regression equation for this data set. e) Using your equation, predict the soup sales on a day where the temperature is 29 degrees.f) Interpret the slope of this equation.g) Can we use this data set and regression equation to predict the sales on a day when the average temperature is 102 degrees? Why or why not?h) What is the R-squared for this equation? How do you interpret that R-squared?
A survey was taken in 2018 that asked people about their saving habits. Researchers wanted to know if people who saved more also spent less. The scatterplot below shows their results when comparing two variables: the amount people reported that they put into savings each month, and the amount they reported that they spent on clothes. The researchers found the correlation coefficient for this data to be -0.239. Which of the following is true about these variables? a. There is no relationship between savings and money spent on clothes each month.b. There is a weak, positive linear relationship between savings and money spent on clothes each month.c. There is a perfect, negative linear relationship between savings and money spent on clothes each month.d. There is a weak, negative linear relationship between savings and money spent on clothes each month.
a. Calculate the sample covariance.X Y-3 -913 95 12b. Calculate the sample correlation.c. Describe in words the information given to us by the sample covariance and by the sample correlation.Explain how the correlation gives us more precise information.d. Explain why the covariance is not a good measure of the strength of the linear relationship between the twovariables.e. Sketch a scatterplot of a bivariate dataset with a correlation of 0.9f. Sketch a scatterplot of a bivariate dataset with a correlation of -0.3.
An economist wants to determine whether there is a linear relationship between a country's gross domestic product (GDP) and carbon dioxide emissions. The data are shown in the table below. c. Compute and interpret the correlation coefficient. d. Compute and interpret the coefficient of determination. e. Test for the significance of the linear relationship. Use a 0.05 level of significance. State your conclusion. Hint: Your conclusion is either of the following. • There is a significant linear relationship between a country's gross domestic product (GDP) and carbon dioxide emissions. • There is no significant linear relationship between a country's gross domestic product (GDP) and carbon dioxide emissions. GDP 1.6 3.6 4.9 1.1 0.9 2.9 2.7 2.3 1.6 1.5 (trillion dollars) Carbon Dioxide Emissions 428.2 828.8 1214.2 444.6 264 415.3 571.8 454.9 358.7 573.5 (millions of metric tons)