A student experimenting with a pendulum counted the number of full swings the pendulur in 20 seconds for various lengths of string. Her data are shown below.
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.
question d but of 48 what is the answer for with 4.
![### Transcription and Explanation of Pendulum Experiment
#### Experiment Overview
A student experimenting with a pendulum recorded the number of full swings the pendulum made in 20 seconds for various lengths of string. The data collected are presented in a table below:
| Length (in.) | 6.5 | 9 | 11.5 | 14.5 | 18 | 21 | 24 | 27 | 30 | 37 | 43 |
|--------------|-----|---|------|------|----|----|----|----|----|----|----|
| Number of swings | 22 | 20 | 17 | 16 | 14 | 13 | 13 | 12 | 11 | 10 | 9 |
#### Analysis
**(a) Explanation of Linear Model Suitability**
- **Scatterplot Analysis**: A linear model is not appropriate because the scatterplot of Length vs. Number of Swings is not linear, even though the correlation coefficient \( r = -0.94 \) and coefficient of determination \( r^2 = 0.89 \) are relatively strong. The residual plot shows a clear pattern, indicating non-linearity.
**(b) Data Re-expression**
- To straighten the scatterplot, the data were re-expressed as \(\log x\) vs. \(\log y\) and \( \frac{1}{x} \) vs. \( y \).
- **\(\log x\) vs. \(\log y\)**: The scatterplot is made more linear with an improved \( r^2 = 0.99 \) and \( r = -0.995 \). Residuals show no clear pattern.
- **\( \frac{1}{x} \) vs. \( y \)**: Shows a reasonable improvement with \( r^2 = 0.96 \) and \( r = 0.982 \). The pattern in residuals is less clear, reducing outliers.
**(c) Appropriate Model Creation**
- The appropriate model derived is:
\[
\log(\text{Number of Swings}) = -0.45(\log(\text{Length})) + 1.721
\]
**(d) Estimation for a 48-inch Pendulum**
- The estimation for the number of swings a pendulum with a 48](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fab37c32c-9a21-47c0-bc8a-d6c0b192dc22%2Febe76570-37a2-490a-b338-4aff06da4e3f%2F78rg4r_processed.png&w=3840&q=75)
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