only the bot can solve this problem.
Jared drew a scatter plot comparing the number of hours worked and the amount of money earned over the course of a week. He graphed the ordered pairs (number of hours, amount paid) for each day worked. Which of the three scatter plots below most likely represents the data?%0D%0A%0D%0AI. %0D%0AII. %0D%0AIII. %0D%0A (1 point)%0D%0A%09I%0D%0A%09II%0D%0A%09III%0D%0A%09none of these
As an AI language model, I do not have access to the scatter plot that Jared drew. Therefore, I cannot determine which of the three scatter plots most likely represents the data. Can you please provide more information or the actual scatter plot for me to analyze?
I. %0D%0AII. %0D%0AIII. can you do any thing with that?
Unfortunately, I am unable to analyze these three options without any additional information or the actual scatter plot. Can you please provide more context or the scatter plot itself for me to provide a more accurate answer?
The following graph shows the number of customers who purchased something from a store in September and October. What is misleading about the graph?%0D%0A%0D%0A (1 point)%0D%0A%09The bars do not touch.%0D%0A%09The intervals on the vertical axis between 200 and 207 are not even.%0D%0A%09The graph exaggerates the difference in the number of customers between September and October.%0D%0A%09The graph is not misleading.
The misleading aspect of the graph is:
- The graph exaggerates the difference in the number of customers between September and October.
The vertical axis does not start at zero, which distorts the scale and makes the difference between the bars appear larger than it actually is. By starting the axis at 200, the difference between the bars is exaggerated visually. This is an example of a misleading graph because the way the data is presented can lead viewers to draw incorrect conclusions about the data being presented.
Erica found the mean of the yearly incomes of a sample of households in her hometown as shown in the chart below. In a report, she stated, “The typical income in my neighborhood is about $81,500 per year.” How is this misleading?%0D%0A%0D%0A $35,000%09 $25,000%09 $28,000%09 $30,000%09 $35,000%0D%0A $225,000%09 $18,000%09 $12,000%09 $25,000%09 $26,000%0D%0A $390,500%09 $16,250%09 $320,325%09 $22,000%09 $29,500%0D%0A $32,300%09 $22,200%09 $18,500%09 $30,500%09 $290,000%0D%0A (1 point)%0D%0A%09The mean of the data is not about $81,500.%0D%0A%09Erica’s statement is not misleading.%0D%0A%09The mean is not a good measure of the incomes because a few higher incomes pull the mean higher than most of the household incomes.%0D%0A%09The mean is not a good measure of the incomes because the lower incomes pull the mean lower than most household incomes.
The statement "The typical income in my neighborhood is about $81,500 per year" is misleading because the mean is not an appropriate measure of a typical income when the income distribution is skewed like the one shown in the chart. It appears that there are only a few households with income significantly higher than the others, which could skew the mean higher than most household incomes. A more appropriate measure of a typical income would be the median, which is not affected by outliers in the same way as the mean. Therefore, Erica's statement is misleading as the mean is not a good measure of typical income for this skewed dataset. The correct answer is:
- The mean is not a good measure of the incomes because a few higher incomes pull the mean higher than most of the household incomes.
A transportation researcher decided to record the number of miles that employees of a large company drove to work each day. The frequency table below displays the data. Which histogram represents the data? # of Miles Tally Frequency 0–5 3 6–10 8 11–15 8 16–20 8 21–25 5 26–30 3 (1 point) The horizontal axis is labeled # of Miles. It is divided into six partitions. The first partition extends from 0 to 5 on the x-axis. The second partition extends from 5 to 10 on the x-axis. The third partition extends from 10 to 15 on the x-axis. The fourth partition extends from 15 to 20 on the x-axis. The fifth partition extends from 20 to 25 on the x-axis. The sixth partition extends from 25 to 30 on the x-axis. The vertical axis is labeled Frequency. It is numbered from 0 to 8 with a step size of 2. Six rectangles or bars are shown. The rectangle spanning the 0 to 5 partition has a height of 3. The rectangle spanning the 5 to 10 partition has a height of 8. The rectangle spanning the 10 to 15 partition has a height of 8. The rectangle spanning the 15 to 20 partition has a height of 8. The rectangle spanning the 20 to 25 partition has a height of 5. The rectangle spanning the 25 to 30 partition has a height of 3. The horizontal axis is labeled # of Miles. It is divided into six partitions. The first partition extends from 0 to 5 on the x-axis. The second partition extends from 5 to 10 on the x-axis. The third partition extends from 10 to 15 on the x-axis. The fourth partition extends from 15 to 20 on the x-axis. The fifth partition extends from 20 to 25 on the x-axis. The sixth partition extends from 25 to 30 on the x-axis. The vertical axis is labeled Frequency. It is numbered from 0 to 8 with a step size of 2. Six rectangles or bars are shown. The rectangle spanning the 0 to 5 partition has a height of 2. The rectangle spanning the 5 to 10 partition has a height of 8. The rectangle spanning the 10 to 15 partition has a height of 8. The rectangle spanning the 15 to 20 partition has a height of 4. The rectangle spanning the 20 to 25 partition has a height of 5. The rectangle spanning the 25 to 30 partition has a height of 3. The horizontal axis is labeled # of Miles. It is divided into five partitions. The first partition extends from 5 to 10 on the x-axis. The second partition extends from 10 to 15 on the x-axis. The third partition
extends from 15 to 20 on the x-axis. The fourth partition extends from 20 to 25 on the x-axis. The fifth partition extends from 25 to 30 on the x-axis. The vertical axis is labeled Frequency. It is numbered from 0 to 8 with a step size of 2. Five rectangles or bars are shown. The rectangle spanning the 5 to 10 partition has a height of 5. The rectangle spanning the 10 to 15 partition has a height of 8. The rectangle spanning the 15 to 20 partition has a height of 4. The rectangle spanning the 20 to 25 partition has a height of 5. The rectangle spanning the 25 to 30 partition has a height of 3.
The histogram that represents the data is:
- The horizontal axis is labeled # of Miles. It is divided into six partitions. The first partition extends from 0 to 5 on the x-axis. The second partition extends from 5 to 10 on the x-axis. The third partition extends from 10 to 15 on the x-axis. The fourth partition extends from 15 to 20 on the x-axis. The fifth partition extends from 20 to 25 on the x-axis. The sixth partition extends from 25 to 30 on the x-axis. The vertical axis is labeled Frequency. It is numbered from 0 to 8 with a step size of 2. Six rectangles or bars are shown. The rectangle spanning the 0 to 5 partition has a height of 3. The rectangle spanning the 5 to 10 partition has a height of 8. The rectangle spanning the 10 to 15 partition has a height of 8. The rectangle spanning the 15 to 20 partition has a height of 8. The rectangle spanning the 20 to 25 partition has a height of 5. The rectangle spanning the 25 to 30 partition has a height of 3.
This histogram correctly represents the frequency distribution of the number of miles driven by employees of a large company to work each day as given in the frequency table.