The line and bar graphs plot the data points against time. Convert these line and bar graphs into data sets you can work with. This, unfortunately, will mean reading the data points of the figure.

First read the following links for figuring out how to calculate IQR, mean, median and standard deviation of a data set.

Mean - https://www.mathsisfun.com/mean.html

Median - https://www.mathgoodies.com/lessons/vol8/median

IQR - https://www.statisticshowto.datasciencecentral.com/probability-and-statistics/interquartile-range/

SD - https://www.dummies.com/education/math/statistics/how-to-calculate-standard-deviation-in-a-statistical-data-set/ (note that average and mean refer to the same quantity)

Here is one of the created data sets for you from the figures (note that these are approximate numbers that I got from simple eyeballing - you can do a better job by using a ruler). The axis and scale for the number of immigrants is on the right side of the figure. The axis and scale for immigrants as percentage is on the left side of the figure

Year Number of Immigrants (in millions)

1850 2.5

1860 4.0

1870 5.0

1880 6.0

1890 9.5

1900 10.0

1910 13.5

1920 14.0

1930 14.5

1940 11.5

1950 10.5

1960 10.0

1970 10.0

1980 14.5

1990 20.0

2000 31.0

2010 40.0

2018 45.0

Now, you have 18 data points in your data set. Simply, use what you know about calculating the mean, median, SD and IQR.

You can generate a data set for immigrants as a percentage in a similar fashion.

For the other figure, you have 4 sets of data.

Data set 1 (African American immigrants as a percentage of total number of immigrants)

1960 0

1970 1

2013 4

Data set 2 (European American immigrants as a percentage of total number of immigrants)

1960 67

1970 58

2013 13

Etc.

Note that for these data sets calculating the mean, median etc. is not useful as there are only 3 data points in each set. You can instead refer to trends such as the percentage of European Americans has declined rapidly, where as the percentage of Latin American immigrants has risen sharply.