The question asks you to identify a trend that best fits the data. 

Over 10 years, the revenue of the company decreases and increases with no clear overall pattern or trend, so the answers claiming that there is a general increase or decrease are incorrect. 

While the revenue was at a high point ($65,000) in the year 1994, it was not the highest point in the 10 years. The highest point was in 1998, with a revenue of $77,000. 

While the revenue did increase from 1991 to 1992, it decreased by even more from 1991 to 1993. Therefore, from 1991 to 1993, there was actually a net decrease in revenue, not a net increase. 

The correct answer is: "The company's revenue generally decreased from 1994 to 1996." Notice the data points matching the years 1994, 1995, and 1996 are linked by a line that has a downward slope. This indicates a general decrease in revenue. 

Notice that the question narrows the time period you are examining to 1994–1998. While there are years with higher incomes than in this time period (namely, 1992 and 1999), we can only consider the years from 1994 to 1998.

The highest point within this period is in the year 1998. Matching this data point with the values on the y-axis (the income values), you will see that this data point lies between $400,000 and $500,000. Looking more closely, you can even say that it is above halfway between the values, so it appears to be greater than $450,000.

Thus, the value that approximates the income in 1998 is $460,000. 

Be careful to take into account the units on each axis. The y-axis measures income in thousands of dollars, so the answer is $460,000 and not $460.

The line graph is best used with data that occurs sequentially, especially sequentially over a period of time. The line graph is best at describing a general trend, such as an increase or decrease, over time.

The decrease in weight of an adult male over 12 months fits a line graph well because there is data that can be organized sequentially (chronologically, in this case) and because we would be looking for some sort of trend in the data, a decrease over time in this case.

Each bar in this histogram represent the percent of employees with salary in the given range. For example, the bar above the label "150–159" means that 25% of the employees earn between $150,000 and $159,000. 

Since we want the percent of employees who earn $180,000 or more, we need to add the values for the bars for "180–189", "190–199", and "200–210".

The bars show that 15% of employees earn between  $180,000 and $189,000, 15% earn between $190,000 and $199,000, and 5% earn between $200,000 and $210,000. 

Adding these percentages together yields 35%.

The question asks for you to find the revenue in the year 2004. The horizontal axis is labeled "Year," so look there first to find where the label for 2004 is.

Tracing upwards, you will see that the value 2004 corresponds to a value of about 16 on the vertical axis. Since the label for the vertical axis is "Dollars in Millions," this means that the revenue in 2004 was 16 million dollars.

In numerical form, 16 million dollars is $16,000,000.

The formula for a regression line for a set of paired data is the line of the equation

,

where

and 

.

, the number of pairs.

 is the sum of the  values:

 is the sum of the  values:

 is the sum of the squares of the  values:

 is the sum of the  products:

Substituting:

 and  are the arithmetic means of the  and  values, respectively - the sums divided by the number of values:

The equation of the linear regression line is .

The formula for a regression line for a set of paired data is the line of the equation

,

where

and 

.

, the number of pairs.

 is the sum of the  values:

 is the sum of the  values:

 is the sum of the squares of the  values:

 is the sum of the  products:

Substituting:

 and  are the arithmetic means of the  and  values, respectively - the sums divided by the number of values:

The linear regression line is the line of the equation .

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:

The die has six sides with the following values: one tow, three, four, five, and six. Of these values the die has three that are even: two, four, and six. We can write the following probability.

Simplify.

Now, let's convert this into a percentage:

Two fair dice can be thrown with 36 equally probable rolls. 18 rolls comprise one even number and one odd number. They are as follows - with the rolls that total seven in boldface:

6 of the 18 possible rolls have total 7, making the probability of a roll of 7, given that exactly one doe shows an even number, equal to  .

Two fair dice can be thrown with 36 equally probable rolls.9 rolls can be thrown that show two odd numbers: they are

This leaves 27 rolls with at least one even number, 6 of which result in a sum of 7:

This makes the probability of rolling a sum of 7

,

the correct choice.