Consider the following measurements of blood hemoglobin concentrations (in g/dL) from three human populations at different geographic locations: population1 = [ 14.7 , 15.22, 15.28, 16.58, 15.10 ] population2 = [ 15.66, 15.91, 14.41, 14.73, 15.09] population3 = [ 17.12, 16.42, 16.43, 17.33] Perform ANOVA to check if any of these populations have different mean hemoglobin concentrations. (Assume that all the ANOVA requirements such as normality, equal variances and random samples are met.) After you perform ANOVA perform a Tukey-Kramer post-hoc test at a significance level of 0.05 to see which populations actually have different means. As usual, round all answers to two digits after the decimal point. (Make sure you round off to at least three digits any intermediate results in order to obtain the required precision of the final answers.) For any questions, which ask about differences in means or test statistics, which depend on differences in means provide absolute values. In other words if you get a negative value, multiply by -1 to make it positive. QUESTION 3 PART A- If you run ANOVA on the data for the three populations, what is the F-ratio? QUESTION 3 PART B- If you run ANOVA on the data for the three populations, what is the range of the P-value
Addition Rule of Probability
It simply refers to the likelihood of an event taking place whenever the occurrence of an event is uncertain. The probability of a single event can be calculated by dividing the number of successful trials of that event by the total number of trials.
Expected Value
When a large number of trials are performed for any random variable ‘X’, the predicted result is most likely the mean of all the outcomes for the random variable and it is known as expected value also known as expectation. The expected value, also known as the expectation, is denoted by: E(X).
Probability Distributions
Understanding probability is necessary to know the probability distributions. In statistics, probability is how the uncertainty of an event is measured. This event can be anything. The most common examples include tossing a coin, rolling a die, or choosing a card. Each of these events has multiple possibilities. Every such possibility is measured with the help of probability. To be more precise, the probability is used for calculating the occurrence of events that may or may not happen. Probability does not give sure results. Unless the probability of any event is 1, the different outcomes may or may not happen in real life, regardless of how less or how more their probability is.
Basic Probability
The simple definition of probability it is a chance of the occurrence of an event. It is defined in numerical form and the probability value is between 0 to 1. The probability value 0 indicates that there is no chance of that event occurring and the probability value 1 indicates that the event will occur. Sum of the probability value must be 1. The probability value is never a negative number. If it happens, then recheck the calculation.
Consider the following measurements of blood hemoglobin concentrations (in g/dL) from three human populations at different geographic locations:
population1 = [ 14.7 , 15.22, 15.28, 16.58, 15.10 ]
population2 = [ 15.66, 15.91, 14.41, 14.73, 15.09]
population3 = [ 17.12, 16.42, 16.43, 17.33]
Perform ANOVA to check if any of these populations have different mean hemoglobin concentrations. (Assume that all the ANOVA requirements such as normality, equal variances and random samples are met.) After you perform ANOVA perform a Tukey-Kramer post-hoc test at a significance level of 0.05 to see which populations actually have different means. As usual, round all answers to two digits after the decimal point. (Make sure you round off to at least three digits any intermediate results in order to obtain the required precision of the final answers.) For any questions, which ask about differences in means or test statistics, which depend on differences in means provide absolute values. In other words if you get a negative value, multiply by -1 to make it positive.
QUESTION 3 PART A- If you run ANOVA on the data for the three populations, what is the F-ratio?
QUESTION 3 PART B- If you run ANOVA on the data for the three populations, what is the
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