The managers of a brokerage firm are interested in finding out if the number of new clients a broker brings into the firm affects the sales generated by the broker.  For the last year, the managers sampled 16 brokers and determined the number of new clients they enrolled during that year and their sales amounts in millions of dollars for that year.  This data is presented in the table that follows.  In your answers, sales should be left as a decimal as in the data table.  It is not necessary to multiply by one million dollars. Perform a simple linear regression analysis of this data and answer the following questions.  Suggestion: Read all of the questions before doing any analysis. Broker New Clients Sales 1 27 5.32 2 11 3.44 3 42 7.96 4 33 6.62 5 15 4.06 6 15 3.16 7 25 4.9 8 36 6.84 9 28 5.8 10 30 5.84 11 17 3.56 12 22 4.58 13 18 3.7 14 24 5.34 15 35 6.9 16 40 7.56   (b) Find the value of the coefficient of determination. (c) Based on the coefficient of determination, would this equation be useful for predicting annual sales?  Simply type 'yes' or 'no'. (d) Find the value of Syx.

Use excel in this question: The Motion Picture Distributors Association of Australia collects yearly data on gross box office takings of the films in Australia. Data is provided here for the years 1990-2004. (Unfortunately more recent data is not readily available.) Is the highest box office takings for a film related to the number of films screened in Australia that year? Year No. of films screened Highest box office ($ million) 1990 252 26.1 1991 240 19.22 1992 229 18.73 1993 259 31.74 1994 252 24.65 1995 253 17.93 1996 281 29.29 1997 285 22.7 1998 273 47.33 1999 255 38.72 2000 250 30.72 2001 245 32.05 2002 258 33.81 2003 268 37.14 2004 318 50.35   1. Produce a scatterplot of the relevant data. Comment on the appropriateness of analysing this data using a linear regression model.  2. Use the statistics from a regression analysis of this data to evaluate the evidence for a useful predictive linear relationship between the two variables at the 5%…

The basketball coach at a local college believes that his team scores more points at home games when more people show up. Below is a list of all home games last year with scores and corresponding attendance. Use Excel, SPSS, or work by hand to show your work finding r. Show your work on the attached pages. Score Attendance   Score Attendance 54 380   67 410 57 350   78 215 59 320   67 113 80 478   56 250 82 451   85 450 75 250   101 489 73 489   99 472 53 451       What is the obtained t-score for this correlation? What is the critical t-score for a two-tailed test with a = 0.05? Is this correlation significant based on the t-scores?

What percentage of data would you predict would be between 25 and 50 and what percentage would you predict would be more than 50 miles? Use the Week 4 spreadsheet again to find the percentage of the data set we expect to have values between 25 and 50 as well as for more than 50. Now determine the percentage of data points in the dataset that fall within each of these ranges, using same strategy as above for counting data points in the data set. How do each of these compare with your prediction and why is there a difference? Predicted percentage between 25 and 50: ________________________ Actual percentage: Predicted percentage more than 50 miles: Actual percentage: ___________________________________________ Comparison ____________________________________________________ Why? __________________________________________________________ Drive: 44 20 88 6 71 42 76 63 61 63 84 28 55 33 88 80 86 83 5 85 25 25 54 54 81 73 29 76 78…

2. The boxplot below represents data collected on some variable of interest. The mean of this data set is 51. The vertical line represents some value which we will call "X". In the table below there are ten numbers, ordered from smallest to largest. For parts a. through f., write down the number that matches the appropriate value from the table. No number will be used more than once, and four numbers will not be used at all. X : 40 45 50 55 60 - 5.9 - 0.63 0.69 5.1 17.8 20.5 48.5 51.9 53.6 56.3 a. Q1 (25th percentile) = b. Median = c. Range = d. Q3 (75th percentile) = e. IQR = f. Proportion of data larger than X= 1

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The box plots below show how fast cars are travelling at a specific time of a day on highway I-5 and l-405 in King county. Fill in the blank. I-405 I-5 45 50 55 60 65 70 75 Speed (miles per hour) If Jay is driving at the median speed on l-5, he is driving faster than % of drivers on l-405.

For a statistics class project, Jonathan and Crystal held an ice-cream-eating contest. They randomly selected 29 males and 35 females from their large high school to participate. Each student was given a small cup of ice cream and instructed to eat it as fast as possible. Jonathan and Crystal then recorded each contestant's gender and time (in seconds), as shown in the dotplots. : ● Male Female 15 75 90 105 30 45 60 Time to eat ice cream (sec) Do these data give convincing evidence of a difference in the population means at the a = 0.10 significance level? State appropriate hypotheses for performing a significance test. Be sure to define the parameters of interest. = Ho: M1 M2 = 0, Ha M₁ −μ₂ > 0, where μ₁ = the true mean time it takes male students to eat a small cup of ice cream and μ₂ = the true mean time it takes for female students to eat a small cup of ice cream. Ho: M1 - M₂ = 0, Ha M₁ M₂ < 0, where μ₁ = the true mean time it takes male students to eat a small cup of ice cream and…

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With the rise of e-books and online retailers, the demand for physical bookstores has been declining. The number of bookstores from 2008 through 2013 (in hundreds) are shown in the following table (x = 0 corresponds to 2008): Year 0 1 2 3 4 5 Number of Bookstores 4.2 3.8 3.4 3.0 2.6 2.2 Using the equation, estimate the number of bookstores in 2016. Group of answer choices 200 300 100 400