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- How would you describe this data?For each of the following variables, determine whether the variable is categorical or numerical. If the variable is numerical, determine whether the variable is discrete or continuous. In addition, determine the measurement scale. a. Time, in hours, spent working per month b. Name of a household's cable television provider c. Number of online purchases made in a month d. Satisfaction rating of a cable television provider (from low to high) e. Time, in hours, spent surfing the Internet per month a. Time, in hours, spent working per month is what kind of variable? O A. This variable is a continuous numerical variable that is ratio-scaled. O B. This variable is a discrete numerical variable that is ratio-scaled. OC. This variable is a discrete numerical variable that is interval-scaled. O D. This variable is a categorical variable that is ordinal-scaled. O E. This variable is a categorical variable that is nominal-scaled. O F. This variable is a continuous numerical variable that is…Ypsilanti Market Research conducted a survey to find out whether people who earn more money purchase more expensive goods. The following graph indicates the relationship between income the survey subjects earned and the price of the car that they purchased. The variable shown on the vertical axis is __________ (options: thousands of dollars per car, thousands of dollars per year, income, thousands of dollars, price, number of goods). The units for the variable on the horizontal axis are _________ (options: thousands of dollars per car, thousands of dollars per year, income, thousands of dollars, price, number of goods). There are two ways to view the information presented on the graph. First, the graph tells us the amount a person with a certain income is likely to spend on a car, and second, it tells us the probable income of a person who spent a certain amount on a car. For example, if an individual earned $50,000 last year and purchased a new car, you would expect that person to…
- You are given the following dataset from a sample of the population. Superhero Number of Villains Captured Number of Losses Average Hours of Sleep Spiderham 30 9 12 Alligator Loki 50 1 23 Iron Man 20 8 6 Phil Coulson 18 2 8 Yelena Belova 25 5 2 Round all answers to 2 decimal places. IE: 3.567 = 3.57 A. What kind of dataset is this? Answer either Panel, Time series, or cross sectional B. Calculate the average number of villains captured in this sample. C. Calculate the variance of the number of villains captured in this sample. D. Calculate the correlation between the number of villains captured and average hours of sleep . If answer is negative, include negative sign. E. Calculate the sample covariance between the average number of losses and average hours of sleep . If answer is negative, include negative sign.Temperature is an example of a variable that uses the ratio scale either the ratio or the ordinal scale the ordinal scale the interval scaleI'm not sure how to graph this and I'm not sure if I am answering the questions correctly
- Your boss would like your help on a marketing research project she is conducting on the relationship between the price of soda and the quantity of soda demanded. She hands you the following document: Price of Soda Quantity of Soda Demanded (Dollars per can) (Billions of cans) 0.50 2,000 0.75 1,500 1.00 1,000 1.25 750 Your task is to take this and construct a graphical representation of the data. In doing so, you determine that as the price of soda rises, the quantity of soda demanded decreases. This confirms theWhat is the difference between an unbiased estimator and a consistentestimator?The following data was collected to explore how a student's age and GPA affect the number of parking tickets they receive in a given year. The dependent variable is the number of parking tickets, the first independent variable (1) is the student's age, and the second independent variable (x2) is the student's GPA. Effects on Number of Parking Tickets Age GPA Number of Tickets 17 2 1 17 2 2 18 2 4 20 2 5 20 3 5 22 3 6 233 22 3 6 3 7 25 4 7 Copy Data Step 2 of 2: Determine if a statistically significant linear relationship exists between the independent and dependent variables at the 0.01 level of significance. If the relationship is statistically significant, identify the multiple regression equation that best fits the data, rounding the answers to three decimal places. Otherwise, indicate that there is not enough evidence to show that the relationship is statistically significant. Answer How to enter your answer (opens in new window) Selecting a checkbox will replace the entered answer…
- The following data was collected to explore how the number of square feet in a house, the number of bedrooms, and the age of the house affect the selling price of the house. The dependent variable is the selling price of the house, the first independent variable (x1) is the square footage, the second independent variable (x2) is the number of bedrooms, and the third independent variable (x3) is the age of the house. Effects on Selling Price of Houses Square Feet Number of Bedrooms Age Selling Price 2750 5 14 296600 2696 5 11 294400 2523 4 7 281400 2057 4 7 240600 1797 4 5 208600 1767 4 5 196400 1684 4 4 171900 1554 3 4 162800 1521 3 3 144900 Copy Data Step 2 of 2: Determine if a statistically significant linear relationship exists between the independent and dependent variables at the 0.01 level of significance. If the relationship is statistically significant, identify the multiple regression equation that best fits the data, rounding the answers to three decimal places. Otherwise,…The following data was collected to explore how the number of square feet in a house, the number of bedrooms, and the age of the house affect the selling price of the house. The dependent variable is the selling price of the house, the first independent variable (x1) is the square footage, the second independent variable (x2) is the number of bedrooms, and the third independent variable (x3) is the age of the house. Effects on Selling Price of Houses Square Feet Number of Bedrooms Age Selling Price 2756 4 14 310300 2721 4 12 291500 2666 4 8 281100 2102 4 7 265600 1880 3 7 237500 1829 3 6 184600 1599 3 4 160800 1422 3 3 132600 1199 3 3 130500 Step 1 of 2: Find the p-value for the regression equation that fits the given data. Round your answer to four decimal places. Copy DataThe numbers of polio cases in the world are shown in the table for various years. Year Number of Polio Cases (thousands) 1988 1992 1996 2000 2005 2007 Let f(t) be the number of polio cases in the world t years since 1980. Use a graphing calculator to draw a scattergram of the data. Is it better to model the data by using a linear or exponential model? Select an answer Find an equation of f. Hint f(t) = 350 138 33 4 3.2 1.3 The number of polio cases Select an answer Hint Round the coefficients to 2 digits. Predict the number of polio cases in 2017. years by Select an answer Predict in which year there will be 1 case of polio. Find the approximate half-life of the number of polio cases. Hint per year.
