We know that the scientific method starts with an observation of a phenomenon. Next, hypotheses are developed and tested using simulations and experiments. Once data are collected they can be used to make conclusions through statistical analysis. In this question, we are asked to analyze the distribution of comparative box plots.

First, let's look at how box plots are used to analyze data.

Box plots are broken into five main parts that create four primary regions. Each region contains a quarter of the data in the plot. The plot is initially broken into two regions using the following values: the minimum value, the maximum value, and the median or average of the set. Next, the minimum and median values are divided by the median of the lower half of the data or the first/lower quartile. Likewise, the median and the maximum values are divided by the median of the upper half of the data set or third/upper quartile. If the box is bigger, then there is a greater variance or spread in the data. Also, if the whiskers are very long, then the data possesses outliers.

Next, let's observe how data are distributed in box plots.

 

A uniform distribution indicates that all values are the same. A symmetrical distribution means that each box in the plot encompasses the same area; furthermore, each whisker (i.e. the line between Q3 and the Maximum as well as the line between Q1 and the Minimum) are the same length. This means that the data is evenly distributed and follows a normal or bell curve distribution. Box plots can also be skewed: either top or bottom skewed. When a plot is bottom or negatively skewed, the distance from the median to the minimum is greater than the distance from the median to the maximum. Even though each box contains the same amount of the data (i.e. twenty-five percent), the box closer to the bottom covers more area than the one closer to the top of the chart. In other words the data is being pulled to the negative end of the chart by outlying data points, while the positive points are clustered closer together at the top of the chart. On the other hand, a plot is top or positively skewed when the distance from the median to the maximum is greater than the distance from the median to the minimum. Even though each box contains the same amount of the data (i.e. twenty-five percent), the box closer to the top covers more area than the one closer to the bottom of the chart. In other words the data is being pulled to the positive end of the chart by outlying data points, while the negative points are clustered closer together at the bottom of the chart.

When data is analyzed in a box plot or chart, it can then be used to make conclusions on the experiment. These conclusions will either support or refute a hypothesis. It is important to note that hypothesis cannot be proven only support can be added for them or against them. Even scientific theories such as gravity are not proven: they are just supported by a wealth of experiments and knowledge.

Now, let's answer the question. When we look at the box plot for the juniors we can see that the distance from the median to the minimum is greater than the distance from the median to the maximum: therefore, the plot is bottom or negatively skewed.

We know that the scientific method starts with an observation of a phenomenon. Next, hypotheses are developed and tested using simulations and experiments. Once data are collected they can be used to make conclusions through statistical analysis. In this question, we are asked to analyze the distribution of comparative box plots.

First, let's look at how box plots are used to analyze data.

Box plots are broken into five main parts that create four primary regions. Each region contains a quarter of the data in the plot. The plot is initially broken into two regions using the following values: the minimum value, the maximum value, and the median or average of the set. Next, the minimum and median values are divided by the median of the lower half of the data or the first/lower quartile. Likewise, the median and the maximum values are divided by the median of the upper half of the data set or third/upper quartile. If the box is bigger, then there is a greater variance or spread in the data. Also, if the whiskers are very long, then the data possesses outliers.

Next, let's observe how data are distributed in box plots.

 

A uniform distribution indicates that all values are the same. A symmetrical distribution means that each box in the plot encompasses the same area; furthermore, each whisker (i.e. the line between Q3 and the Maximum as well as the line between Q1 and the Minimum) are the same length. This means that the data is evenly distributed and follows a normal or bell curve distribution. Box plots can also be skewed: either top or bottom skewed. When a plot is bottom or negatively skewed, the distance from the median to the minimum is greater than the distance from the median to the maximum. Even though each box contains the same amount of the data (i.e. twenty-five percent), the box closer to the bottom covers more area than the one closer to the top of the chart. In other words the data is being pulled to the negative end of the chart by outlying data points, while the positive points are clustered closer together at the top of the chart. On the other hand, a plot is top or positively skewed when the distance from the median to the maximum is greater than the distance from the median to the minimum. Even though each box contains the same amount of the data (i.e. twenty-five percent), the box closer to the top covers more area than the one closer to the bottom of the chart. In other words the data is being pulled to the positive end of the chart by outlying data points, while the negative points are clustered closer together at the bottom of the chart.

When data is analyzed in a box plot or chart, it can then be used to make conclusions on the experiment. These conclusions will either support or refute a hypothesis. It is important to note that hypothesis cannot be proven only support can be added for them or against them. Even scientific theories such as gravity are not proven: they are just supported by a wealth of experiments and knowledge.

Now, let's answer the question. When we look at the box plot for the juniors we can see that the distance from the median to the minimum is less than the distance from the median to the maximum: therefore, the plot is top or positively skewed.

We know that the scientific method starts with an observation of a phenomenon. Next, hypotheses are developed and tested using simulations and experiments. Once data are collected they can be used to make conclusions through statistical analysis. In this question, we are asked to analyze the distribution of comparative box plots.

First, let's look at how box plots are used to analyze data.

Box plots are broken into five main parts that create four primary regions. Each region contains a quarter of the data in the plot. The plot is initially broken into two regions using the following values: the minimum value, the maximum value, and the median or average of the set. Next, the minimum and median values are divided by the median of the lower half of the data or the first/lower quartile. Likewise, the median and the maximum values are divided by the median of the upper half of the data set or third/upper quartile. If the box is bigger, then there is a greater variance or spread in the data. Also, if the whiskers are very long, then the data possesses outliers.

Next, let's observe how data are distributed in box plots.

 

A uniform distribution indicates that all values are the same. A symmetrical distribution means that each box in the plot encompasses the same area; furthermore, each whisker (i.e. the line between Q3 and the Maximum as well as the line between Q1 and the Minimum) are the same length. This means that the data is evenly distributed and follows a normal or bell curve distribution. Box plots can also be skewed: either top or bottom skewed. When a plot is bottom or negatively skewed, the distance from the median to the minimum is greater than the distance from the median to the maximum. Even though each box contains the same amount of the data (i.e. twenty-five percent), the box closer to the bottom covers more area than the one closer to the top of the chart. In other words the data is being pulled to the negative end of the chart by outlying data points, while the positive points are clustered closer together at the top of the chart. On the other hand, a plot is top or positively skewed when the distance from the median to the maximum is greater than the distance from the median to the minimum. Even though each box contains the same amount of the data (i.e. twenty-five percent), the box closer to the top covers more area than the one closer to the bottom of the chart. In other words the data is being pulled to the positive end of the chart by outlying data points, while the negative points are clustered closer together at the bottom of the chart.

When data is analyzed in a box plot or chart, it can then be used to make conclusions on the experiment. These conclusions will either support or refute a hypothesis. It is important to note that hypothesis cannot be proven only support can be added for them or against them. Even scientific theories such as gravity are not proven: they are just supported by a wealth of experiments and knowledge.

Now, let's answer the question. When we look at the box plot for the seniors we can see that the distance from the median to the minimum is less than the distance from the median to the maximum: therefore, the plot is top or positively skewed.

We know that the scientific method starts with an observation of a phenomenon. Next, hypotheses are developed and tested using simulations and experiments. Once data are collected they can be used to make conclusions through statistical analysis. In this question, we are asked to analyze the distribution of comparative box plots.

First, let's look at how box plots are used to analyze data.

Box plots are broken into five main parts that create four primary regions. Each region contains a quarter of the data in the plot. The plot is initially broken into two regions using the following values: the minimum value, the maximum value, and the median or average of the set. Next, the minimum and median values are divided by the median of the lower half of the data or the first/lower quartile. Likewise, the median and the maximum values are divided by the median of the upper half of the data set or third/upper quartile. If the box is bigger, then there is a greater variance or spread in the data. Also, if the whiskers are very long, then the data possesses outliers.

Next, let's observe how data are distributed in box plots.

 

A uniform distribution indicates that all values are the same. A symmetrical distribution means that each box in the plot encompasses the same area; furthermore, each whisker (i.e. the line between Q3 and the Maximum as well as the line between Q1 and the Minimum) are the same length. This means that the data is evenly distributed and follows a normal or bell curve distribution. Box plots can also be skewed: either top or bottom skewed. When a plot is bottom or negatively skewed, the distance from the median to the minimum is greater than the distance from the median to the maximum. Even though each box contains the same amount of the data (i.e. twenty-five percent), the box closer to the bottom covers more area than the one closer to the top of the chart. In other words the data is being pulled to the negative end of the chart by outlying data points, while the positive points are clustered closer together at the top of the chart. On the other hand, a plot is top or positively skewed when the distance from the median to the maximum is greater than the distance from the median to the minimum. Even though each box contains the same amount of the data (i.e. twenty-five percent), the box closer to the top covers more area than the one closer to the bottom of the chart. In other words the data is being pulled to the positive end of the chart by outlying data points, while the negative points are clustered closer together at the bottom of the chart.

When data is analyzed in a box plot or chart, it can then be used to make conclusions on the experiment. These conclusions will either support or refute a hypothesis. It is important to note that hypothesis cannot be proven only support can be added for them or against them. Even scientific theories such as gravity are not proven: they are just supported by a wealth of experiments and knowledge.

Now, let's answer the question. When we look at the box plot for the sophomores we can see that the distance from the median to the minimum is greater than the distance from the median to the maximum: therefore, the plot is bottom or negatively skewed.

We know that the scientific method starts with an observation of a phenomenon. Next, hypotheses are developed and tested using simulations and experiments. Once data are collected they can be used to make conclusions through statistical analysis. In this question, we are asked to analyze the distribution of comparative box plots.

First, let's look at how box plots are used to analyze data.

Box plots are broken into five main parts that create four primary regions. Each region contains a quarter of the data in the plot. The plot is initially broken into two regions using the following values: the minimum value, the maximum value, and the median or average of the set. Next, the minimum and median values are divided by the median of the lower half of the data or the first/lower quartile. Likewise, the median and the maximum values are divided by the median of the upper half of the data set or third/upper quartile. If the box is bigger, then there is a greater variance or spread in the data. Also, if the whiskers are very long, then the data possesses outliers.

Next, let's observe how data are distributed in box plots.

 

A uniform distribution indicates that all values are the same. A symmetrical distribution means that each box in the plot encompasses the same area; furthermore, each whisker (i.e. the line between Q3 and the Maximum as well as the line between Q1 and the Minimum) are the same length. This means that the data is evenly distributed and follows a normal or bell curve distribution. Box plots can also be skewed: either top or bottom skewed. When a plot is bottom or negatively skewed, the distance from the median to the minimum is greater than the distance from the median to the maximum. Even though each box contains the same amount of the data (i.e. twenty-five percent), the box closer to the bottom covers more area than the one closer to the top of the chart. In other words the data is being pulled to the negative end of the chart by outlying data points, while the positive points are clustered closer together at the top of the chart. On the other hand, a plot is top or positively skewed when the distance from the median to the maximum is greater than the distance from the median to the minimum. Even though each box contains the same amount of the data (i.e. twenty-five percent), the box closer to the top covers more area than the one closer to the bottom of the chart. In other words the data is being pulled to the positive end of the chart by outlying data points, while the negative points are clustered closer together at the bottom of the chart.

When data is analyzed in a box plot or chart, it can then be used to make conclusions on the experiment. These conclusions will either support or refute a hypothesis. It is important to note that hypothesis cannot be proven only support can be added for them or against them. Even scientific theories such as gravity are not proven: they are just supported by a wealth of experiments and knowledge.

Now, let's answer the question. When we look at the box plot for the juniors we can see that the distance from the median to the minimum is less than the distance from the median to the maximum: therefore, the plot is top or positively skewed.

We know that the scientific method starts with an observation of a phenomenon. Next, hypotheses are developed and tested using simulations and experiments. Once data are collected they can be used to make conclusions through statistical analysis. In this question, we are asked to analyze the distribution of comparative box plots.

First, let's look at how box plots are used to analyze data.

Box plots are broken into five main parts that create four primary regions. Each region contains a quarter of the data in the plot. The plot is initially broken into two regions using the following values: the minimum value, the maximum value, and the median or average of the set. Next, the minimum and median values are divided by the median of the lower half of the data or the first/lower quartile. Likewise, the median and the maximum values are divided by the median of the upper half of the data set or third/upper quartile. If the box is bigger, then there is a greater variance or spread in the data. Also, if the whiskers are very long, then the data possesses outliers.

Next, let's observe how data are distributed in box plots.

 

A uniform distribution indicates that all values are the same. A symmetrical distribution means that each box in the plot encompasses the same area; furthermore, each whisker (i.e. the line between Q3 and the Maximum as well as the line between Q1 and the Minimum) are the same length. This means that the data is evenly distributed and follows a normal or bell curve distribution. Box plots can also be skewed: either top or bottom skewed. When a plot is bottom or negatively skewed, the distance from the median to the minimum is greater than the distance from the median to the maximum. Even though each box contains the same amount of the data (i.e. twenty-five percent), the box closer to the bottom covers more area than the one closer to the top of the chart. In other words the data is being pulled to the negative end of the chart by outlying data points, while the positive points are clustered closer together at the top of the chart. On the other hand, a plot is top or positively skewed when the distance from the median to the maximum is greater than the distance from the median to the minimum. Even though each box contains the same amount of the data (i.e. twenty-five percent), the box closer to the top covers more area than the one closer to the bottom of the chart. In other words the data is being pulled to the positive end of the chart by outlying data points, while the negative points are clustered closer together at the bottom of the chart.

When data is analyzed in a box plot or chart, it can then be used to make conclusions on the experiment. These conclusions will either support or refute a hypothesis. It is important to note that hypothesis cannot be proven only support can be added for them or against them. Even scientific theories such as gravity are not proven: they are just supported by a wealth of experiments and knowledge.

Now, let's answer the question. When we look at the box plot for the juniors we can see that the distance from the median to the minimum is greater than the distance from the median to the maximum: therefore, the plot is bottom or negatively skewed.

We know that the scientific method starts with an observation of a phenomenon. Next, hypotheses are developed and tested using simulations and experiments. Once data are collected they can be used to make conclusions through statistical analysis. In this question, we are asked to analyze the distribution of comparative box plots.

First, let's look at how box plots are used to analyze data.

Box plots are broken into five main parts that create four primary regions. Each region contains a quarter of the data in the plot. The plot is initially broken into two regions using the following values: the minimum value, the maximum value, and the median or average of the set. Next, the minimum and median values are divided by the median of the lower half of the data or the first/lower quartile. Likewise, the median and the maximum values are divided by the median of the upper half of the data set or third/upper quartile. If the box is bigger, then there is a greater variance or spread in the data. Also, if the whiskers are very long, then the data possesses outliers.

Next, let's observe how data are distributed in box plots.

 

A uniform distribution indicates that all values are the same. A symmetrical distribution means that each box in the plot encompasses the same area; furthermore, each whisker (i.e. the line between Q3 and the Maximum as well as the line between Q1 and the Minimum) are the same length. This means that the data is evenly distributed and follows a normal or bell curve distribution. Box plots can also be skewed: either top or bottom skewed. When a plot is bottom or negatively skewed, the distance from the median to the minimum is greater than the distance from the median to the maximum. Even though each box contains the same amount of the data (i.e. twenty-five percent), the box closer to the bottom covers more area than the one closer to the top of the chart. In other words the data is being pulled to the negative end of the chart by outlying data points, while the positive points are clustered closer together at the top of the chart. On the other hand, a plot is top or positively skewed when the distance from the median to the maximum is greater than the distance from the median to the minimum. Even though each box contains the same amount of the data (i.e. twenty-five percent), the box closer to the top covers more area than the one closer to the bottom of the chart. In other words the data is being pulled to the positive end of the chart by outlying data points, while the negative points are clustered closer together at the bottom of the chart.

When data is analyzed in a box plot or chart, it can then be used to make conclusions on the experiment. These conclusions will either support or refute a hypothesis. It is important to note that hypothesis cannot be proven only support can be added for them or against them. Even scientific theories such as gravity are not proven: they are just supported by a wealth of experiments and knowledge.

Now, let's answer the question. When we look at the box plot for the sophomores we can see that the distance from the median to the minimum is greater than the distance from the median to the maximum: therefore, the plot is bottom or negatively skewed.

We know that the scientific method starts with an observation of a phenomenon. Next, hypotheses are developed and tested using simulations and experiments. Once data are collected they can be used to make conclusions through statistical analysis. In this question, we are asked to analyze the distribution of comparative box plots.

First, let's look at how box plots are used to analyze data.

Box plots are broken into five main parts that create four primary regions. Each region contains a quarter of the data in the plot. The plot is initially broken into two regions using the following values: the minimum value, the maximum value, and the median or average of the set. Next, the minimum and median values are divided by the median of the lower half of the data or the first/lower quartile. Likewise, the median and the maximum values are divided by the median of the upper half of the data set or third/upper quartile. If the box is bigger, then there is a greater variance or spread in the data. Also, if the whiskers are very long, then the data possesses outliers.

Next, let's observe how data are distributed in box plots.

 

A uniform distribution indicates that all values are the same. A symmetrical distribution means that each box in the plot encompasses the same area; furthermore, each whisker (i.e. the line between Q3 and the Maximum as well as the line between Q1 and the Minimum) are the same length. This means that the data is evenly distributed and follows a normal or bell curve distribution. Box plots can also be skewed: either top or bottom skewed. When a plot is bottom or negatively skewed, the distance from the median to the minimum is greater than the distance from the median to the maximum. Even though each box contains the same amount of the data (i.e. twenty-five percent), the box closer to the bottom covers more area than the one closer to the top of the chart. In other words the data is being pulled to the negative end of the chart by outlying data points, while the positive points are clustered closer together at the top of the chart. On the other hand, a plot is top or positively skewed when the distance from the median to the maximum is greater than the distance from the median to the minimum. Even though each box contains the same amount of the data (i.e. twenty-five percent), the box closer to the top covers more area than the one closer to the bottom of the chart. In other words the data is being pulled to the positive end of the chart by outlying data points, while the negative points are clustered closer together at the bottom of the chart.

When data is analyzed in a box plot or chart, it can then be used to make conclusions on the experiment. These conclusions will either support or refute a hypothesis. It is important to note that hypothesis cannot be proven only support can be added for them or against them. Even scientific theories such as gravity are not proven: they are just supported by a wealth of experiments and knowledge.

Now, let's answer the question. When we look at the box plot for the sophomores we can see that the distance from the median to the minimum is greater than the distance from the median to the maximum: therefore, the plot is bottom or negatively skewed.

In order to solve this problem, we need to discuss probabilities. A probability is generally defined as the chances or likelihood of an event occurring. It is calculated by identifying two components: the event and the sample space. The event is defined as the favorable outcome or success that we wish to observe. On the other hand, the sample space is defined as the set of all possible outcomes for the event. Mathematically we calculate probabilities by dividing the event by the sample space:

Let's use a simple example: the rolling of a die. We want to know the probability of rolling a one. We know that the sample space is six because there are six sides or outcomes to the die. Also, we know that there is only a single side with a value of one; therefore,

Now, let's convert this into a percentage:

Probabilities expressed in fraction form will have values between zero and one. One indicates that an event will definitely occur, while zero indicates that an event will not occur. Likewise, probabilities expressed as percentages possess values between zero and one hundred percent where probabilities closer to zero are unlikely to occur and those close to one hundred percent are more likely to occur. 

Now, we can discuss other terms related with probabilities. The complement of an event is the outcomes that do not include the event. In other words, what is the probability of not rolling one? For instance, in the previous example, we wanted to know the probability of rolling a one on a die. The complement of this event would be rolling everything but a one. The event in the complement would be rolling a two, three, four, five, or a six. We can calculate the probability using the following method:

Convert to a percentage.

We can also find the complement by subtracting the probability of the event from the maximum probability value of one:

It is important to note that in this case the complement is more likely to occur than the original event. 

Next, lets discuss unions and intersections. Regarding probabilities, a union is the likelihood that one event or another will occur. For instance, what is the probability that a person will role a one or a six on a die? 

Convert to a percentage.

Last, an intersection is the likelihood that one event will occur with another. What is the probability that we will roll a one and flip a coin to reveal "heads"? We know that the probability of rolling a one is:

Now, let's determine the probability of flipping a coin to reveal the "heads" side. A coin has two sides: heads or tails. The probability of rolling heads is as follows:

An intersection is the probability of two events occurring simultaneously; therefore, we need to multiply the probabilities.

Convert to a percentage.

Let's look at a table that contains these specific probabilities and the key words that are indicative of each. 

Now, let's use this question to solve the problem. The question asks us to find the probability of an intersection: cars that are royal blue with V8 engines. 

Convert to a percentage.