In a previous quarter, students were not given thorough directions for how to make the sucrose standard solutions, so the quality of the plots varied significantly. Choose the comment for each plot that best reflects the quality of that plot at the prompt in the Group column. Which calibration plot should be used ✓ [Select] Click here if you cannot view th 2 4

A data set contains the observations 8,5,4,6,9. find ( ∑x )^2

If you want to draw scatter plot where sales is dependent and advert_expenditure is independent varable, you write . Select one: a. plot(sales,advert_expenditure) b. scatter(advert_expenditure,sales) c. plot(advert_expenditure,sales) d. scatter(sales,advert_expenditure)

True or false: in a Dot Plot, the number of dots in a vertical column represents the number of times a certain value appears in a data set.

A medical statistician wanted to examine the relationship between the amount of sunshine (x) and incidence of skin cancer (y). As an experiment he found the number of skin cancers detected per 100,000 of population and the average daily sunshine in eight counties around the country. These data are shown below.Draw a scatter plot of the data and comment on the relationship between the 2 variables, predicting what the skin cancer rate might be for 10 hours of daily sunshine.

8, 9, 10, 12, 18, 22, 23, 25, 25, 27, 28, 29, 31, 39, 39, 43. Notice that the numbers are ordered from least to greatest. Make a box-and-whisker plot for the data.

An insurance company determines that a linear relationship exists between the cost of fire damage in major residential fıres and the distance from the house to the nearest fire station. A sample of 15 recent fires in a large suburb of a major city was selected. For each fire, the following variables were recorded: x= the distance between the fire and the nearest fire station (in miles) y= cost of damage (in dollars) The distances between the fire and the nearest fire station ranged between 0.7 miles and 6.1 miles. The correlation between cost and distance is 0.961. Test if the correlation is significant at a=.10. O No, the correlation is not significant because 0.961 does not exceed the critical value. O Yes, the correlation is significant because 0.961 exceeds the critical value. No, the correlation is not significant because 0.961 exceeds the critical value. O Yes, the correlation is significant because 0.961 does not exceed the critical value. O No, the correlation is not…

Identify the type of data that would be used when the variable of interest is most-watched TV show.

4.3) •VA report from Texas Transportation Institute (Texas A&M University System 2005) on Congestion Re- duction Strategies looked into the extra travel time (due to traffic congestion) for commute travel per traveler per year in hours for different urban areas. Below are the data for urban areas that had a population of over 3 million for the year 2002. Extra Hours per Traveler per Year Urban Area Los Angeles San Francisco 98 75 Washington DC Atlanta 66 64 Houston 65 Dallas, Fort Worth Chicago Detroit 61 55 54 Miami 48 Boston 53 New York 50 Phoenix 49 Philadelphia 40 4. Compute the mean and median values for extra travel hours. Based on the values of the mean and median, is the distribution of extra travel hours likely to be approximately symmetric, positively skewed, or negatively skewed?

A frequency table is likely to be included in a research article when there are only two values for a variable of interest. if there are a very small number of scores. when complex statistical results are reported. to show participant demographic characteristics.