The regression model y = 5.5(1.025)* represents the number of items sold in millions (y) by a company each year (x) after 2010. Select all of the appropriate statements that apply to the model. O The number of items sold each year grows by 2.5%. O There were originally 550,000 items sold. O There were approximately 4.3 million items sold in 2000. O There will be approximately 7 million items sold in 2020. O There will be approximately 8 million items sold in 2030.

Airborne concentrations of a pollutant follow a lognormal distribution. The pollutant has concentrations that exceed 1.5 ppm 33% of the time, and exceed 6.025 ppm 2.5% of the time. What fraction of the time is 13.934 ppm exceeded?

The data show the bug chirps per minute at different temperatures. Find the regression​ equation, letting the first variable be the independent​ (x) variable. Find the best-predicted temperature for a time when a bug is chirping at the rate of 3000chirps per minute. Use a significance level of 0.05. What is wrong with this predicted​ value?   Chirps in 1 min 1004 952 1237 1116 1177 1249   Temperature ​(°​F) 83.1 76 95.1 87.5 92.1 88.4 What is the regression​ equation?   (^ over y)=_____+_____x ​(Round the​ x-coefficient to four decimal places as needed. Round the constant to two decimal places as​ needed.)   What is the best-predicted temperature for a time when a bug is chirping at the rate of 3000chirps per​ minute?   The best-predicted temperature when a bug is chirping at 3000 chirps per minute is ____°F. ​(Round to one decimal place as​ needed.)   What is wrong with this predicted​ value? Choose the correct answer below.   A. It is…

Data was recorded for the temperature, in degrees Celsius, of a cup of coffee over a 30-minute period. Given the regression equation, In(Temp) = 4.20 0.023(Time), what is the predicted temperature after 3 minutes? 33.45 °C 62.24 °C 65.17 °C 66.69 °C

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Bankers at a large financial institution created the linear regression model d = 0.37 – 0.0004s to predict the proportion of customers who would default on their loans, d, based on the customer's credit score, s. For a customer with a credit score of 700, which of the following is true? A The default proportion is predicted to be 0.09. В The default proportion will be 0.09. The default proportion is predicted to be approximately 1.75 million. The default proportion will be approximately 1.75 million. E The default proportion is predicted to be 0.28.

Table 3 gives the market value of a luxury sedan (in dollars) xyears after its purchase. Find an exponential regression model of the form y= ab* for this data set. Estimate the purchase price of the sedan. Estimate the value of the sedan 10 years after its purchase. Round answers to the nearest dollar. TABLE 3 Value ($) 1 23,125 19,050 3 15,625 11,875 5 9,450 6 7,125 2.

In the following time series linear regression equation, t is time in years and y is sales in millions of dollars: ýt = Interpret the regression coefficient. = 11. 602 + 3.46t For every one year increase in time, sales increase by $3.46 million on average. For every one year increase in time, sales decrease by $3.46 million on average. As time increases, sales fluctuate around a constant mean of $3.46 million. For every one year increase in time, sales decrease by $346 million on average.

A regression was run to determine if there is a relationship between hours of TV watched per day (x) and the number of sit-ups a person can do (y). The results were: Y a+bx b = -0.79 = 23.04 a r² = 0.4493 r = -0.6703 a. If a person watches 10 hours of television a day, predict how many sit-ups he can do. b. If a person can do 7 sit ups, predict how many hours of television a day they watch. hours

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