How many deviations above the mean

An unbiased statistic:

Accurately estimates a population parameter

The expected value of M reflects:

Average of all possible sample means

A standardized score reflects:

Raw score position relative to mean

Range in statistics is defined as:

Highest value minus lowest value

A weighted mean is used when:

Values have different importance

SS in statistics represents:

Sum of squared deviations from the mean

The law of large numbers ensures that:

Average of results gets closer to expected value

An example of a biased statistic is:

Using N instead of N-1 in variance

A simple random sample is obtained by:

Choosing without any preference

Is scored below by that percentage of data

A sampling distribution is:

The distribution of a statistic across samples

Without replacement, each draw:

Increases chances for remaining units

Deviation scores tell us:

Distance each value is from the mean

Population standard deviation (σ) is:

Square root of population variance

The Central Limit Theorem states:

Sample means approximate normal distribution

May not reflect true population parameter

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Chapter 7 Final Exam Review + Chapter 6 Final Exam Review + Chapter 5 Final Exam Review + Chapt

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Questions 93

Answers

1
In a symmetrical distribution, mean is:
Unpredictable
Always zero
Equal to the median
Different from median
2
A z-score indicates:
Total deviations
The raw score itself
Variability in the data
How many deviations above the mean
3
Independence in sampling prevents:
Data from being collected
Sample interdependence
Equal opportunity
Random selection
4
A deviation score is calculated as:
Score minus the mean
Score times the standard deviation
Score divided by the variance
Score plus the mean
5
An unbiased statistic:
Always uses N-1
Accurately estimates a population parameter
Has no variability
Ignores sample size
6
A small σ indicates:
Small variability
No variability
Large variability
Negative variability
7
The expected value of M reflects:
Lowest sample mean
Highest sample mean
Average of all possible sample means
Sum of all sample values
8
A standardized score reflects:
Variance of raw scores
Median position of data
Total count of scores
Raw score position relative to mean
9
A score at the 85th percentile means:
85% score below this score
It is the median score
85% score above this score
15% score above this score
10
Sum of squares (SS) is used to calculate:
Range
Mode
Variance
Median
11
Standardized distributions typically have a standard deviation of:
More than 1
The same as the mean
0
1
12
High standard deviation means:
Data is not variable
Data has no outliers
Data points are far from mean
Data points are close to mean
13
Range in statistics is defined as:
Highest value minus lowest value
Sum of all values
Average of top and bottom values
Median of the data
14
A weighted mean is used when:
All values are equal
Values have different importance
Calculating the median
Data are symmetrical
15
SS in statistics represents:
Total number of data points
Squared sum of ranges
Sum of all values
Sum of squared deviations from the mean
16
The law of large numbers ensures that:
Smaller samples reflect the pop. more closely
Average of results gets closer to expected value
Sample means deviate more over time
Variability increases with more trials
17
Sample variance is calculated by:
Multiplying SS by N
Dividing SS by N
Dividing SS by N-1
Multiplying SS by N-1
18
An example of a biased statistic is:
Sample mean
Using N instead of N-1 in variance
Sample variance
Population mean
19
The symbol μ represents:
Median
Sample mean
Weighted mean
Population mean
20
Random sampling is important because it:
·Requires less data
Increases bias
Reduces bias
Simplifies calculations
21
A simple random sample is obtained by:
Choosing without any preference
Picking every nth unit
Sampling the nearest units
Selecting the largest units
22
Standardized scores are also known as:
Deviation scores
Raw scores
Z-scores
Median scores
23
Sample variance is unbiased because:
It uses N-1 in the denominator
It excludes outliers
It is always positive
It uses N
24
Variability in a dataset refers to:
The lowest data value
How close data values are to the mean
How spread out data values are
The highest data value
25
The purpose of z-score transformation is:
Normalize distribution
Inflate deviations
Measure central tendency
Standardize variance
26
The expected value of M (sample mean) is:
The sample median
The population mean
Always 0
The mode of the sample
27
An independent random sample ensures:
One sample affects another
·Only relevant data is collected
No sample influences another
Results are connected
28
A percentile:
Measures variability
Reflects the exact score
Is scored below by that percentage of data
Divides data into 100 equal parts
29
A standardized distribution has a mean of:
Variable, depending on data
0
The same as raw scores
1
30
A sampling distribution is:
A single sample's distribution
The distribution of a statistic across samples
The spread of one sample's data
Only used in non-parametric tests
31
Standard deviation is:
Variance squared
Square root of the variance
Variance divided by mean
Average deviation from the median
32
Without replacement, each draw:
Decreases diversity
Increases chances for remaining units
·Has no effect on other units
Allows repeated selections
33
Deviation scores tell us:
How data clusters
Distance each value is from the mean
The central tendency
The variance of data
34
Percentile rank shows:
How a score compares to others
The average score
·The highest score
The median score
35
A skewed distribution indicates:
Data leaning towards one side
Equal mean and median
Two peaks
No variability
36
Population variance (σ²) measures:
Average data points
Average of squared deviations
Data point range
Total data points
37
Population standard deviation (σ) is:
Square root of population variance
The variance
Average population data point
Mean deviation of the population
38
Independent random sampling means each sample:
Is dependent on size
Influences another
Is chosen separately
Is chosen consecutively
39
Which measure indicates variability?
Range
Mode
Median
Mean
40
In a multimodal set, the major mode is:
The most common value
The smallest value
The median value
The largest value
41
The Central Limit Theorem states:
Variance is irrelevant
All samples will be normal
Population must be normal
Sample means approximate normal distribution
42
The shape of the sampling distribution depends on:
·The method of data collection
The sample size
The hypothesis being tested
The type of data collected
43
Central tendency is measured by:
Variance, standard deviation
Histogram, bar graph
Mean, median, mode
Correlation, regression
44
Median effectively represents the center in:
Skewed distributions
Symmetrical distributions
Bimodal distributions
Nominal data
45
The probability of a certain event is always:
·Negative
More than 1
·Zero
Between 0 and 1
46
A biased statistic:
May not reflect true population parameter
Is always accurate
Always overestimates parameters
Reflects sample accurately
47
The sample mean M is calculated from:
A subset of the population
The mode
A population
Multiple populations
48
Degrees of freedom in sample variance is:
N-1
N/2
N
N+1
49
Population mean μ is:
Average of a sample
Median of population
Average of entire population
Mode of population
50
Which is a measure of central tendency?
Mean
Standard deviation
Variance
Range
51
The major mode is the:
Average mode
Mode with highest frequency
Least frequent mode
Only mode in data
52
When sampling with replacement, each selection:
Depends on the first selection
Reduces the population
·Has fewer choices
Is independent of previous selections
53
Symmetrical distribution is characterized by:
Uneven peaks
Skewed tails
Equal halves mirrored
One peak
54
Z-score is calculated by:
(Raw score - Mean) / Standard deviation
(Standard deviation - Raw score) / Mean
(Raw score - Median) / Range
(Mean - Raw score) / Variance
55
As the sample size increases, the distribution of sample means:
Shows more variability
Approaches a uniform distribution
Becomes more normal
Becomes more skewed
56
According to the law of large numbers, as N increases:
The mean becomes less accurate
The mean approximation improves
The central limit theorem is invalid
Sampling error increases
57
Z-score transformation is used to:
Reduce data to raw scores
Calculate raw score deviations
Adjust scores to a mean of 0
Increase variability
58
The standard error of M decreases with:
Larger sample size
Increased population variance
More biased samples
Smaller sample size
59
Variance measures:
Frequency of the mean
Difference between high and low values
Average distance from the mean
Spread of middle 50% of data
60
Sample standard deviation (s) is:
Average deviation from mean
The square root of sample variance
Median of squared deviations
The square of variance
61
The 30th percentile indicates:
Top 30% of scores
30% score above this point
70% score above this point
30% score below this point
62
Probability measures the likelihood of:
An event repeating
Sampling error
Ignoring an outcome
An event occurring
63
A low variance indicates:
Data points are close to the mean
No outliers
Data points spread out
Many outliers
64
Sample variance differs from population variance in:
Its calculation of mean
Divisor used (N-1 for sample)
Using absolute values
Not squaring deviations
65
Sampling without replacement means each unit:
Is replaced after selection
Is chosen more than once
Is not included in the sample
Can only be chosen once
66
The mode is the:
Average of all values
Least occurring value
Most frequently occurring value
Middle value
67
The standard error of M indicates:
Mean error in sampling
Variability of sample means
Total error in the sample
Bias in the sample
68
Sampling error refers to:
Variability due to sample choice
Bias in question design
Errors in hypothesis testing
Mistakes in data collection
69
A high value of s indicates:
Data points close to each other
No outliers
Uniform data distribution
Data points far apart
70
The symbol M means
Population average
Mode of the sample
Median of the sample
Sample average
71
Degrees of freedom are used to:
Reduce data values
Standardize tests
Calculate mean
Adjust sample calculations
72
In selection, random sampling ensures:
·Only selected units are sampled
Every unit has an equal chance
Some units have more chances
Units are sampled based on size
73
Sampling with replacement means:
Units are chosen only once
·Units can't be chosen again
Each unit can be chosen more than once
Only first unit is replaced
74
Independent sampling ensures:
Dependent results
·Biased results
Each sample doesn't affect others
·Samples are connected
75
The minor mode is:
Median of modes
Most frequent mode
Only mode in data
Least frequent mode
76
Central Limit Theorem applies when sample size is:
Large
Less than 30
Exactly 30
Small
77
A random sample is characterized by:
·Biased selection
·Sampling only large units
Selective sampling
Equal chance of selection
78
A bimodal distribution has:
Three peaks
No clear peaks
Two peaks
One peak
79
σ² is calculated by summing:
Squares of deviations from mean, divided by N
All data values
Data ranges
All deviations
80
The distribution of sample means:
Has less variability as N increases
Is always normally distributed
Equals the population variance
Varies widely with sample size
81
A raw score represents:
The mean value
Scores above average
Adjusted data points
Data before processing
82
The median is:
The middle value when sorted
The most frequent value
Always the largest value
The average value
83
A deviation score in statistics is:
The difference from the mean
The average of all scores
Always zero
A fixed percentage
84
In a simple random sample, each member
Has a unique selection method
Is chosen based on attributes
Has an equal chance of selection
Is chosen with replacement
85
Multimodal means the distribution has:
Uniform peaks
One distinct peak
Multiple peaks
No peaks
86
Weighted mean considers:
Values and their frequency
Only the highest values
Values below the median
Values above the mode
87
Deviation scores show:
Distance from mean
Distance from range
Distance from mode
Distance from median
88
How can sampling error be reduced?
Smaller sample sizes
Less frequent sampling
Larger sample sizes
Fewer variables
89
The unit normal table helps in calculating:
Sample variances
Mean deviations
Probabilities related to the normal curve
Exact values of standard deviations
90
In bimodal data, the minor mode is:
The higher peak
The mode closest to mean
The lower peak
The average of peaks
91
A unit normal table is used to find:
Variance estimates
Probabilities for Z-scores
Sample means
Percentiles for raw scores
92
Which best describes a random sample?
Always accurate
Focused on specific units
Always large
Representative due to random selection
93
What is a raw score in data analysis?
Modified data value
Standardized value
Mean of scores
Original, unmodified score

Chapter 7 Final Exam Review + Chapter 6 Final Exam Review + Chapter 5 Final Exam Review + Chapt Flashcards | StudBud