In Exercises 21 and 22, determine whether the approximate shap...

Question

In Exercises 21 and 22, determine whether the approximate shape of the distribution in the histogram is symmetric, uniform, skewed left, skewed right, or none of these.

Histogram displaying frequency distribution with peaks around 22 and 26, illustrating data variability.

Accepted Answer

Hello there. Today we're gonna solve the following practice problem together. So first off, let us read the problem and highlight all the key pieces of information that we need to use in order to solve this problem. The histogram given below shows the number of pets owned by families in a neighborhood. What is the best description of the distribution's shape? Awesome. So it appears for this particular problem, we're trying to determine what is the best description of the distribution shape provided this histogram that is given to us by the problem itself. So looking at our histogram, we have our bars that are represented in beige. We also have our Y axis, which is our vertical axis, which represents the number of families. Then we have our X axis, the horizontal axis, that represents the number of pets. So it appears that 4 families have 1 pet. And So, 4 families has 21 pet, and then we have 3 families have 2 pets, then we have Two families have 3 pets, and then we have one family that has 4 pets, and it just significantly starts to go lower and lower and lower, so it looks like steps on a ladder going or not on a ladder, steps on a stairs going down. So now that we have a good visualization of what's happening for this particular prompt, let's take a moment to read off our multiple choice answers to see what our final answer might be. A is left skewed, B is symmetric, C is uniform, and D is right skewed. Awesome. So first off, in order to determine the shape of the distribution using our given histogram that is provided to us by the problem itself, we need to analyze the provided data. So let us note that our X axis states the number of pets which we have 1234, and 5, and then we have our Y axis, which is Once again, let's be very clear. Our X axis is our number of pets, and our Y axis is the number of families, and we're told the number of families are 4321, and 0. Fantastic. So let's make a few observations. Firstly, the highest part of the bar is at 1 pet, which I'll call P for short. So we have 1 pet for 4 families, which I'll call F for families for short, so F for families for short. Fantastic. Our second observation is the bars decrease in height as the number of pets increases. So 3 families have 2 pets, and 2 families have 3 pets, and 1 family has 4 pets. Fantastic, like we discussed earlier, and 0 families have 5 pets. Fantastic. And our 3rd step I, or rather not our third step, our third observation is the distribution shows a clear decline from left to right without any gaps or spikes. So, with that in mind, to make our final conclusions about the shape of the distribution, We can safely say, as we should recall, based on all the information that we know, that our distribution shape is right skewed or positively skewed because the tail of the distribution extends to the right, indicating that fewer families, so fewer families will own a higher number of pets. The majority of the data is concentrated on the left side. So once again we can safely say that our distribution shape is right skewed or positively skewed because the tail of our distribution extends to the right, which indicates that fewer families own a higher number of pets, meaning the majority of the data is. Concentrated on the left side. So with that said, our final answer without a doubt has to be multiple choice answer letter D, which once again is right skewed. Thank you so much for watching. Hopefully that helped, and I can't wait to see you in the next video. Bye.

In Exercises 21 and 22, determine whether the approximate shape of the distribution in the histogram is symmetric, uniform, skewed left, skewed right, or none of these.

Key Concepts

Histogram

Distribution Shape

Skewness

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