IM Alg1.3.4 Practice: Linear Models

The scatter plot shows the number of times a player came to bat and the number of hits they had.
[img]data:image/png;base64,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[/img][br][br]The scatter plot includes a point at [math]\left(318,80\right)[/math]. Describe the meaning of this point in this situation.
The scatter plot shows the number of minutes people had to wait for service at a restaurant and the number of staff available at the time.
[img]data:image/png;base64,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[/img][br][br][size=150]A line that models the data is given by the equation [math]y=-1.62x+18[/math], where [math]y[/math] represents the wait time, and [math]x[/math] represents the number of staff available.[/size][br][br]The slope of the line is -1.62. What does this mean in this situation? Is it realistic?[br]
The [math]y[/math]-intercept is [math]\left(0,18\right)[/math]. What does this mean in this situation? Is it realistic?[br]
A taxi driver records the time required to complete various trips and the distance for each trip.
[img]data:image/png;base64,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[/img][br][br]The best fit line is given by the equation [math]y=0.467x+0.417[/math], where [math]y[/math] represents the distance in miles, and [math]x[/math] represents the time for the trip in minutes.[br][br]Use the best fit line to estimate the distance for a trip that takes 20 minutes. Show your reasoning.
Use the best fit line to estimate the time for a trip that is 6 miles long. Show your reasoning.
Data is collected about the number of wins and losses by a random sample of teams with an animal mascot and those with another kind of mascot.
[size=150]The column relative frequencies are shown in the table.[/size][br]Based on the information in the table, is there an association between the variables? Explain your reasoning.[br][br][center][img]data:image/png;base64,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[/img][/center]
A random selection of indoor and outdoor pool managers are surveyed about the number of people in each age group that swim there.
[size=150]Results from the survey are displayed in the two-way table.[/size][br][br][center][img]data:image/png;base64,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[/img][/center]Based on the data, does there appear to be an association between pool type and age group? Explain your reasoning. 
Data from a random sample of people are collected about how they watch movies in the genres of action or mystery.
Which value would best fit in the missing cell to suggest there is no association between the genre and how the movies are watched? [br][br][center][img]data:image/png;base64,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[/img][/center]
Close

Information: IM Alg1.3.4 Practice: Linear Models