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| 7648473384 | Histograms | Good for displaying large quantities of data; Provide view of the data density. Given a histogram, you can sketch the corresponding box plot; but given a box plot, you can't sketch the corresponding histogram. |  | 0 |
| 7648477387 | Standard Frequency Histograms | Better at showing distribution | 1 | |
| 7648479759 | Cumulative Frequency Histograms | Better at showing percentile; shows the cumulative or total frequency achieve by each bin, rather than the frequency of that particular bin. |  | 2 |
| 7648486778 | Relative Frequency | Term for percent or proportion. Tells us how large a number is relative to the total. You can make regular/cumulative relative frequency histograms. |  | 3 |
| 7648495877 | Left/Right Endpoint Inclusive | When the data is inclusive of one side of the bin (0-9) v. (1-10) | 4 | |
| 7648514496 | Box and Whiskers Plot | Summarizes a data set using five summary statistics while also plotting outliers. The whiskers attempt to capture all of the data remaining outside the box plot, except outliers. |  | 5 |
| 7648521765 | Five Number Summary | Minimum, Maximum, and the three quartiles (Q1, Q2, Q3) | 6 | |
| 7648527134 | Q1 | Represents the first quartile, which is the 25th percentile, and is the median of the smaller half of the data | 7 | |
| 7648531936 | Q2 | Represents the second quartile. Equivalent to the 50th percentile (median) | 8 | |
| 7648536973 | Q3 | Represents the third quartile, or 75th percentile, also is the median of the larger half of the data set | 9 | |
| 7648540853 | IQR Outliers | Upper Bound: 1.5 * IQR above Q3 Lower Bound: 1.5 * IQR below Q1 | 10 | |
| 7648546688 | Dot Plots | Simple graph for numerical data, uses data to show the frequency (number of occurrences) of the values in a data set. Shows the distribution (frequency of corresponding values). Kind of like a histogram. |  | 11 |
| 7648559673 | Split Stem and Leaf | Like a regular stem and leaf, but used when there are too many numbers on one row, or there are only a few stems. Split each row into two halves, with the leaves from 0-4 on the first half, and the leaves from 5-9 on the second half |  | 12 |
| 7648572129 | Stem and Leaf | Each number is broken up into two parts. Stem: first part, and consists of the beginning digits Leaf: second part and consists of the final digits. seen as more detailed because every value is given |  | 13 |
| 7648578394 | Scatter Plots | Provides a case by case view of data that illustrates the relationship between two numerical variables. Paired Data |  | 14 |
| 7648584612 | Paired Data | When the two observations correspond to each other. Scatterplots. | 15 | |
| 7648591724 | Standard Deviation | Average distance that observations fall from the mean. Easier to interpret because it has the same units as the data set. Associated with the mean. Remember: Range doesn't equal spread. | 16 | |
| 7648612045 | Properties of the Standard Deviation | Adding by a constant does not affect the SD. Multiplying by a constant does affect the SD SD can't be negative (because of the square in the equation) SD can equal zero (when all the data is on one point) | 17 | |
| 7648632555 | How to Calculate SD by Hand | 1. Find the deviations from the mean for each X values (deviations from the average, will always add up to 0). 2. Square each of those deviations. 3. Add those numbers up (gives you a single number) 4. Divide by (n-1) --> This gives you the variance 5. Square root the variance to get the SD | 18 | |
| 7648652577 | SD Equation | 19 | ||
| 7648656291 | Empirical Rule | ~68% of the data will be within one SD of the mean ~95% of the data will be within two SDs of the mean ~99.7 of the data will be within three SDs of the mean Rule of thumb: Outliers are more than 2 SDs above/below the mean. |  | 20 |
| 7648667703 | Interquartile Region (Definition + Equation) | Tells us the spread of the middle 50% of the data. Associated with median IQR = Q3 - Q1 | 21 | |
| 7648676968 | Z- Scores (Definition + Equation) | Tells us how many SD's is a point from the mean. If we add all the Z scores up they equal to 0. Z-Scores = Standard Units (No units) Z = (x - xbar)/sd | 22 | |
| 7648689894 | Mean (Definition + Equation) | Common way to measure the center of a distribution of data. Average. Mean: Xbar = 1/n * Sigma * Xi (add all the values up, and divide by the number of numbers) | 23 | |
| 7648701465 | X Bar | Sample mean | 24 | |
| 7648701466 | Mu (μ) | Population mean | 25 | |
| 7648706957 | Sigma (Σ) | Sum | 26 | |
| 7648708587 | Properties of the Mean | Sensitive to outliers. Adding by a constant affects the mean (shifts the values) Multiplying by a constant affects the mean (stretches or contracts the values) The mean follows the tail: - right skewed: mean > median. - left skewed: mean < median - symmetric distribution: mean = median (approx) | 27 | |
| 7648725162 | Median (Definition + Equation + Properties) | The number in the middle. In an ordered data set, the median is the observation right in the middle. If even: median is the average of the n/2 and n/2+1 values If odd: median is the (n+1)/2 value Outliers have a smaller effect on the median (robust) | 28 | |
| 7648744373 | Mean v. Median in Graphs | Right skewed: mean > median. Left skewed: mean < median Symmetric distribution: mean = median (approx) |  | 29 |
| 7648748295 | Distribution | refers to the values that a variable takes and the frequency of those values | 30 | |
| 7648751799 | Modality | Where are the peaks. Unimodal, Bimodal, Multimodal, Uniform | 31 | |
| 7648754467 | Shape (Skew) | How the graph looks Left, Right, Symmetric | 32 | |
| 7648757723 | Measures of Center | Mean/ Median | 33 | |
| 7648757724 | Measures of Spread | SD/ IQR | 34 | |
| 7648760222 | When Comparing Distributions | Compare with respect to center, spread, and shape. Include any unusual observations (outliers) Use the CONTEXT of the question | 35 | |
| 7648767854 | Outliers | Observations that appears extreme relative to the rest of the data. Helpful for: - identifying asymmetry in the distribution - identifying data collection or entry errors. - providing insight into interesting properties of the data. | 36 | |
| 7648782059 | Comparing Distribution v. Looking at Association | Distribution: compare center, spread, shape. to compare distribution visually, we use two single variable graphs (histograms, dot plots, box plots, back to back stem and leaf) Association: We look for a positive, negative, or no relationship between the variables. To see the association visually, we require a scatter plot | 37 |
