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WorksheetsAP Stats Unit 9
Total questions: 15
Worksheet time: 15mins
A bag contains 9 green marbles, 5 yellow marbles and 6 red marbles. You choose one marble. What is the probability of selecting a green or red marble? Do you add or multiply probabilities?
Add
Multiply
A bag contains 9 green marbles, 5 yellow marbles and 6 red marbles. You choose one marble. What is the probability of selecting a green or red marble?
8/15
6/11
3/4
2/3
Leon throws a biased coin. The probability of getting tails is 0.4.
Work out the probability of getting heads.
0.4
0.6
0.8
0.2
Find the probability of drawing a 10 from a standard deck of 52 cards.
4 out of 52
13 out of 52
13/52
4/52
Clara picks a marble at random, puts it back, and then picks another marble at random.
Dependent
Independent
The outcome of one event does influence the outcome of the other event.
mutually exclusive event
probability
dependent event
simple probability
What would be the probability of flipping 10 coins that all land on tails?
10/1024
1/1024
1/1025
10/1024
Ten cards numbered 1-10 are placed in a hat. What is the probability of randomly drawing a card with an even number then a card with a number greater than or equal to five if the first card is replaced?
1/4
2/3
3/10
3/5
What is the probability of rolling a dice and landing on a 4, and then rolling the dice again and landing on any even number?
1 ⁄ 6
1 ⁄ 8
1 ⁄ 2
1 ⁄ 12
Which statements below about least-squares regression are correct?
Only I is correct.
Only II is correct.
Only III is correct.
Both II and III are correct.
All three statements—I, II, and III—are correct.
In a statistics course, a linear regression equation was computed to predict the final exam score from the score on the first test. The equation was yˆ =10+0.9x where y is the final-exam score and x is the score on the first test. Carla scored 95 on the first test. What is the predicted value of her score on the final exam?
85.5
90
9
95.5
none of these
In a statistics course, a linear regression equation was computed to predict the final exam score from the score on the first test. The equation was yˆ =10+0.9x where y is the final-exam score and x is the score on the first test. Bill scored a 90 on the first test and a 93 on the final exam. What is the value of his residual?
–2.0
2.0
3.0
93
none of these
The correlation between the heights of fathers and the heights of their (fully grown) sons is r = 0.52. This value was based on both variables being measured in inches. If fathers' heights were measured in feet (one foot equals 12 inches), and sons' heights were measured in furlongs (one furlong equals 7920 inches), the correlation between heights of fathers and heights of sons would be
much smaller than 0.52
slightly smaller than 0.52
unchanged: equal to 0.52
slightly larger than 0.52
much larger than 0.52
A least-squares regression line for predicting weights of basketball players on the basis of their heights produced the residual plot below. What does the residual plot tell you about the linear model?
A residual plot is not an appropriate means for evaluating a linear model.
The curved pattern in the residual plot suggests that there is no association between the weight and height of basketball players.
The curved pattern in the residual plot suggests that the linear model is not appropriate.
There are not enough data points to draw any conclusions from the residual plot.
The linear model is appropriate, because there are approximately the same number of points above and below the horizontal line in the residual plot.
One concern about the depletion of the ozone layer is that the increase in ultraviolet (UV) light will decrease crop yields. An experiment was conducted in a green house where soybean plants were exposed to varying levels of UV, measured in Dobson units. At the end of the experiment the yield (kg) was measured. A regression analysis was performed with the following results: The least-squares regression line is the line that
minimizes the sum of the distances between the actual UV values and the predicted UV values.
minimizes the sum of the squared residuals between the actual yield and the predicted yield.
minimizes the sum of the distances between the actual yield and the predicted UV.
minimizes the sum of the squared residuals between the actual UV reading and the predicted UV values.
minimizes the perpendicular distance between the regression line and each data point.
