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What else can I help you with?
When to you use a z-scores or t-scores?
T score is usually used when the sample size is below 30 and/or when the population standard deviation is unknown.
How to calculate the degree of freedom to the dependent t-test?
You should think of a dependent t as being a single-sample t on the difference scores. This gives it 1 less than the number of differences as the df. Say you have before/after scores for 10 people. You have 20 scores, but the test is done on the differences, of which you have 10 and that means 9 df. You typically obtain df from n - 1, as you do in this case, you just need to be careful to think of this as the number of pairs and not scores.
How do you to get the probability in normal distribution?
You calculate the z-scores and then use published tables.
How does central tendency compare in distributions based on individual scores and distributions based on sample means?
Central tendency in distributions of individual scores can be influenced by outliers and skewness, leading to potential misrepresentation of the data's central value. In contrast, distributions based on sample means tend to be more stable and normally distributed due to the Central Limit Theorem, which states that as sample size increases, the sample means will cluster around the population mean. Consequently, the mean of sample means will typically provide a more accurate estimate of the population mean than the mean of individual scores, especially in larger samples. Thus, sample means generally offer a more reliable indication of central tendency in aggregate data.
How many scores in 60 years?
To determine how many scores are in 60 years, we first define a "score" as 20 years. Therefore, to find the number of scores in 60 years, we divide 60 by 20, which equals 3. Thus, there are 3 scores in 60 years.
How do you calculate Z and T scores?
z=(x-mean)/(standard deviation of population distribution/square root of sample size) T-score is for when you don't have pop. standard deviation and must use sample s.d. as a substitute. t=(x-mean)/(standard deviation of sampling distribution/square root of sample size)
A population has a mean of m equals 4 and SX equals 48 How many scores are in this population?
If, by SX, is meant the sum of the scores, then the answer is 48/4 = 12
15 dozens equals x if scores equals 9 equals 7?
Sorry this is meaningless. What exactly is the question?
When to you use a z-scores or t-scores?
T score is usually used when the sample size is below 30 and/or when the population standard deviation is unknown.
How to calculate the degree of freedom to the dependent t-test?
You should think of a dependent t as being a single-sample t on the difference scores. This gives it 1 less than the number of differences as the df. Say you have before/after scores for 10 people. You have 20 scores, but the test is done on the differences, of which you have 10 and that means 9 df. You typically obtain df from n - 1, as you do in this case, you just need to be careful to think of this as the number of pairs and not scores.
If a sample of n 15 scores has been organized in a grouped frequency distribution table it is possible to obtain a list of all 15 scores from the table?
true
Given that Evelina's bowling scores were 133 155 176 and 132 find her average score The rule for calculating the average of a set of scores is Average Score equals Sum of Scores Number of Scores?
149
The proportion of scores less than z equals 0.00 is?
0.50
What is the variance of of the scores 3 4 8 and 9?
sum of scores: 24 mean of scores : 24/4 = 6 squared deviations from the mean: 9, 4,4,9 sum of these: 26 sample variance: 26/4 = 6.5
Which sample would produce an expected value of 20?
n= 25 scores from a population with mean =20
How do you to get the probability in normal distribution?
You calculate the z-scores and then use published tables.
How does central tendency compare in distributions based on individual scores and distributions based on sample means?
Central tendency in distributions of individual scores can be influenced by outliers and skewness, leading to potential misrepresentation of the data's central value. In contrast, distributions based on sample means tend to be more stable and normally distributed due to the Central Limit Theorem, which states that as sample size increases, the sample means will cluster around the population mean. Consequently, the mean of sample means will typically provide a more accurate estimate of the population mean than the mean of individual scores, especially in larger samples. Thus, sample means generally offer a more reliable indication of central tendency in aggregate data.
