Suppose you are examining the correlation between two quantitative variables, and the correlation, r, is very small. However, you expected it to be larger. What could you do? Examine the data to determine whether there are any outliers that could be removed. If so, remove the outliers and recalculate r. Change the units of measurement to something else (for example, convert data measured in inches to centimeters). Plot the data on a smaller scale. None of the above

Below is the data for speed vs fuel consumption. The first set is speed(x); the second is fuel (y). if x = 10, y = 21 if x = 20 y =13, if x=30 y=10 if x = 40 y =8 if x = 50 y=7 if x =60 y = 5.9 if x = 70 y = 6.3 if x = 80 y = 6.95 if x = 90 y = 7.57 if x = 100 y = 8.27 if x = 110 y = 9.03 if x = 120 y = 9.87 if x = 130 y = 10.79 if x = 140 y = 11.77 and if x = 150 y =12.83. The equation of the LSR line is y^ = 11.058 - .015x The average of (x, y) are (80, 9.9) respectively. Find another point using the LSR equation, and graph the line

The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. I 11.9 8.3 7.1 3.5 2.4 2.8 2.2 0.4 Y 14.4 11.2 10.3 7.2 6.1 6.2 6.1 4.7 I thousands of automatic weapons y = murders per 100,000 residents This data can be modeled by the equation ŷ = 0.86x + 4.14. Use this equation to answer the following. A) How many murders per 100,000 residents can be expected in a state with 4.1 thousand automatic weapons? Answer = B) How many murders per 100,000 residents can be expected in a state with 10.7 thousand automatic weapons? Answer Round to 3 decimal places. = Round to 3 decimal places.

Create a histogram for each set of data. For the first two problems, the frame is set up for you. For #3, YOU will need to determine the best ways to number the axes. Don't forget to include a title as well! Daily high temperature in degrees Fahrenheit:       63        70        64        71        70        62        68        67        68        72        65             62        59        58        60        59        56        53        51        55        56        50             53        57        55        50        46        49        46        52        48                      Frequency Table: Interval # of values 40-44   45-49   50-54   55-59   60-64   65-69   70-74     2. Test scores, out of 100 points    92 84 95 77 74 80 95 70 66 73 68 90 78 64 72 78 76 65 59 71 77 92 91 89 74 76 90

A study by Judge and Cable (2010) suggests that there is a positive relationship between weight and income for a group of men. The following data is similar to what was collected in the study. To simplify the weight variable, the men are categorized into five categories that measure actual weight relative to height from 1=thinnest to 5=heaviest. Income is recorded as thousands earned annually. Weight (X) Income (Y) 4 151 5 88 3 52 2 73 1 49 3 92 1 56 5 143 a. Calculate the Pearson correlation for these data. b. Is the correlation statistically significant? Use a two-tailed test with a=.05.

Need help with part d only? The slope and average is?

The second slide is the first part of the question  and the first slide is the second part of the question

It has been determined that there is a significant relationship between the temperature (x) and the number of times that a cricket chirps in a minute (y). Below is a table of values: Number of Temperature chirps 69.7 882 93.3 1188 84.3 1104 76.3 864 88.6 1200 82.6 1032 71.6 960 79.6 900

We want to predict the selling price of a house in Newburg Park, Florida, based on the distance the house lies from the beach. Suppose that we're given the data in the table below. These data detail the distance from the beach (x, in miles) and the selling price (y, in thousands of dollars) for each of a sample of fifteen homes sold in Newburg Park in the past year. The data are plotted in the scatter plot in Figure 1. Also given is the product of the distance from the beach and the house price for each of the fifteen houses. (These products, written in the column labelled "xy", may aid in calculations.) Distance from the beach,  x (in miles) Selling price,  y (in thousands of dollars) xy 5.0 270.1 1350.5 11.5 205.9 2367.85 5.9 309.4 1825.46 12.2 200.6 2447.32 2.6 307.1 798.46 5.9 266.0 1569.4 8.3 297.3 2467.59 18.3 224.2 4102.86 6.5 242.4 1575.6 12.1 192.6 2330.46 11.6 229.4 2661.04 9.5 230.9 2193.55 10.1 277.0 2797.7 13.5 270.8 3655.8 6.2…

For problems 1 and 2, draw/sketch a scatter plot that represents the data. Then use your calculator to find and clearly identify the correlation coefficient and then describe the type of correlation that exists between the data. Finally, find the line of best fit.