which of the following are binomial experiments or can be reduced to binomial experiments?
surveying 100 people to determine if they like sudsy soap
tossing a coin 100 times to see how many heads occur
drawing a card with replacement from a deck and getting a heart
asking 1000 people which brand of cigarettes
testing four different brands of aspirin to see which brands are effective
the expected value of a random variable can be thought of as a long-run average
the number of courses a student is taking this semester is an example of a continuous random variable
when the binomial distribution is used, the outcomes must be dependent
a binomial experiment has a fixed number of trials
what is the sum of the probabilities of all outcomes in a probability distribution
0
1/2
1
it cannot be determined
how many outcomes are there in a binomial experiment
2
it varies
the number of trials for a binomial experiment
can be infinite
is unchanged
is unlimited
must be fixed
the total area under a normal distribution is infinite
the standard normal distribution is a continuous distribution
all variables that are approximately normally distributed can be transformed to standard normal variables
the z value corresponding to a number below the mean is always negative
the area under the standard normal distribution to the left of z=0 is negative
the central limit theorem applies to means of samples selected from different populations
the mean of the standard normal distribution is
100
variable
approximately what percentage of normally distributed data values will fall within 1 standard deviation above or below the mean?
68%
95%
99.7%
which is not a property of the standard normal distribution?
it's symmetric about the mean
it's uniform
it's bell shaped
its unimodal
when a distribution is positively skewed, the relationship of the mean, median, and mode from left to right will be?
mean, median, mode
mode, median, mean
median, mode, mean
mean, mode, median
the standard deviation of all possible sample means equals
the population standard deviation
the population standard deviation divided by the population mean
the population standard deviation divided by the square root of the sample size
the square root of the population standard deviation
Interval estimates are preferred over point estimates since the confidence level can be specified
for a specific confidence interval, the larger the sample size, the smaller the margin of error will be.
an estimator is consistent if as the sample size decreases, the value of the estimator approaches the value of the parameter estimated
to determine the sample size needed to estimate a parameter, you must know the margin of error
when a 99% confidence interval is calculated instead of a 95% confidence interval with n being the same, the margin of error will be
smaller
larger
the same
the best point of estimate of the population mean is
the sample mean
the sample median
the sample mode
the sample midrange
when the population standard deviation is unknown and the sample size is less than 30, what table value should be used in computing a confidence interval for a mean
z
t
chi-square
none of the above
No error is committed when the null hypothesis is rejected when it is false
when you are conducting the t test, the population must be approximately normally distributed
the test value separates the critical region from the noncritical region
the values of chi-square test cannot be negative
the chi-square test for variance is always one tailed
when the value of alpha is increased, the probability of committing a type 1 error is
decreased
increased
if you wish to test the claim that the mean of the population is 100, the appropriate null hypothesis is
x(bar) = 100
µ ≥ 100
µ ≤ 100
µ = 100
the degrees of freedom for the chi-square test for variances or standard deviation are
n
n - 1
for the t test, one uses ________ instead of σ
s
X²
a negative relationship between two variables means that for the most part, as the x variable increases, the y variable decreases
a correlation coefficient of -1 implies a perfect linear relationship between the variables
even if the correlation coefficient is high (near +1) or low (near -1), it may not be significant
when the correlation coefficient is significant, you can assume x causes y
it is not possible to have a significant correlation by chance alone
in multiple regression, there are several dependent variables and one independent variable
the strength of the linear relationship between two quantitative variables is determined by the value of
r
a
x
s (subscript "est")
to test the significance of r, a(n) ____ test is used.
F
the test of significance for r has _______ degrees of freedom.
n - 2
the equation of the regression line used in statistics is
x = a + by
y = bx + a
y' = a +bx
x = ay + b
the coefficient of determination is
r²
b
