To check pain-relieving medications for potential side effects on blood pressure, it is decided to give equal doses of each of four medications to test subjects. To control for the potential effect of weight, subjects are classified by weight groups. Subjects are approximately the same age and are in general good health. Two subjects in each category are chosen at random from a large group of male prison volunteers. Subjects’ blood pressures 15 minutes after the dose are shown below. Research question: Is mean blood pressure affected by body weight and/or by medication type? i only need the final two sections answered pertaining to the Turkey comparisons. Systolic Blood Pressure of Subjects (mmHg) Ratio of Subject’s Weightto Normal Weight MedicationM1 MedicationM2 MedicationM3 MedicationM4 Under 1.1 131 146 140 130 135 136 132 125 1.1 to 1.3 136 138 134 131 145 145 147 133 1.3 to 1.5 145 149 146 139 152 157 151 141 Click here for the Excel Data File d) Perform Tukey multiple comparison tests. (Input the mean values within the input boxes of the first row and input boxes of the first column. Round your t-values and critical values to 2 decimal places and other answers to 1 decimal place.) Post hoc analysis for Factor 1: Tukey simultaneous comparison t-values (d.f. = 12) 1.1 or Less 1.1 to 1.3 1.3 to 1.5 1.1 or Less 1.1 to 1.3 1.3 to 1.5 Critical values for experimentwise error rate: 0.05 0.01 Post hoc analysis for Factor 2: Tukey simultaneous comparison t-values (d.f. = 12) Med 4 Med 1 Med 3 Med 2 Med 4 Med 1 Med 3 Med 2 Critical values for experimentwise error rate: 0.05 0.01
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.
To check pain-relieving medications for potential side effects on blood pressure, it is decided to give equal doses of each of four medications to test subjects. To control for the potential effect of weight, subjects are classified by weight groups. Subjects are approximately the same age and are in general good health. Two subjects in each category are chosen at random from a large group of male prison volunteers. Subjects’ blood pressures 15 minutes after the dose are shown below. Research question: Is
i only need the final two sections answered pertaining to the Turkey comparisons.
| Systolic Blood Pressure of Subjects (mmHg) | ||||
| Ratio of Subject’s Weight to Normal Weight |
Medication M1 |
Medication M2 |
Medication M3 |
Medication M4 |
| Under 1.1 | 131 | 146 | 140 | 130 |
| 135 | 136 | 132 | 125 | |
| 1.1 to 1.3 | 136 | 138 | 134 | 131 |
| 145 | 145 | 147 | 133 | |
| 1.3 to 1.5 | 145 | 149 | 146 | 139 |
| 152 | 157 | 151 | 141 | |
Click here for the Excel Data File
d) Perform Tukey multiple comparison tests. (Input the mean values within the input boxes of the first row and input boxes of the first column. Round your t-values and critical values to 2 decimal places and other answers to 1 decimal place.)
Post hoc analysis for Factor 1:
| Tukey simultaneous comparison t-values (d.f. = 12) | ||||
| 1.1 or Less | 1.1 to 1.3 | 1.3 to 1.5 | ||
| 1.1 or Less | ||||
| 1.1 to 1.3 | ||||
| 1.3 to 1.5 | ||||
| Critical values for experimentwise error rate: | ||||
| 0.05 | ||||
| 0.01 | ||||
Post hoc analysis for Factor 2:
| Tukey simultaneous comparison t-values (d.f. = 12) | |||||
| Med 4 | Med 1 | Med 3 | Med 2 | ||
| Med 4 | |||||
| Med 1 | |||||
| Med 3 | |||||
| Med 2 | |||||
| Critical values for experimentwise error rate: | |||||
| 0.05 | |||||
| 0.01 | |||||
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