|
314 | 314 | "- ** mother**: The height of the mother.\n", |
315 | 315 | "- **midparentHeight**: The mid-point between the father and mother's heights (calculated as *(father + 1.08 x mother) ÷ 2*)\n", |
316 | 316 | "- **children**: The total number of children in the family.\n", |
317 | | - "- **childNum**: The number of the child to whom this observation pertains (Galton numbered the children in desending order of height, with male children listed before female children)\n", |
| 317 | + "- **childNum**: The number of the child to whom this observation pertains (Galton numbered the children in descending order of height, with male children listed before female children)\n", |
318 | 318 | "- **gender**: The gender of the child to whom this observation pertains.\n", |
319 | 319 | "- **childHeight**: The height of the child to whom this observation pertains.\n", |
320 | 320 | "\n", |
321 | | - "It's worth noting that there are several distinct types of data recorded here. To begin with, there are some features that represent *qualities*, or characteristics of the child - for example, gender. Other feaures represent a *quantity* or measurement, such as the child's height. So broadly speaking, we can divide data into *qualitative* and *quantitative* data.\n", |
| 321 | + "It's worth noting that there are several distinct types of data recorded here. To begin with, there are some features that represent *qualities*, or characteristics of the child - for example, gender. Other features represent a *quantity* or measurement, such as the child's height. So broadly speaking, we can divide data into *qualitative* and *quantitative* data.\n", |
322 | 322 | "\n", |
323 | 323 | "#### Qualitative Data\n", |
324 | 324 | "Let's take a look at qualitative data first. This type of data is categorical - it is used to categorize or identify the entity being observed. Sometimes you'll see features of this type described as *factors*. \n", |
325 | 325 | "##### Nominal Data\n", |
326 | 326 | "In his observations of children's height, Galton assigned an identifier to each family and he recorded the gender of each child. Note that even though the **family** identifier is a number, it is not a measurement or quantity. Family 002 it not \"greater\" than family 001, just as a **gender** value of \"male\" does not indicate a larger or smaller value than \"female\". These are simply named values for some characteristic of the child, and as such they're known as *nominal* data.\n", |
327 | 327 | "##### Ordinal Data\n", |
328 | | - "So what about the **childNum** feature? It's not a measurement or quantity - it's just a way to identify individual children within a family. However, the number assigned to each child has some additional meaning - the numbers are ordered. You can find similar data that is text-based; for example, data about training courses might include a \"level\" attribute that indicates the level of the course as \"basic:, \"intermediate\", or \"advanced\". This type of data, where the value is not itself a quantity or measurement, but it indicates some sort of inherent order or heirarchy, is known as *ordinal* data.\n", |
| 328 | + "So what about the **childNum** feature? It's not a measurement or quantity - it's just a way to identify individual children within a family. However, the number assigned to each child has some additional meaning - the numbers are ordered. You can find similar data that is text-based; for example, data about training courses might include a \"level\" attribute that indicates the level of the course as \"basic:, \"intermediate\", or \"advanced\". This type of data, where the value is not itself a quantity or measurement, but it indicates some sort of inherent order or hierarchy, is known as *ordinal* data.\n", |
329 | 329 | "#### Quantitative Data\n", |
330 | 330 | "Now let's turn our attention to the features that indicate some kind of quantity or measurement.\n", |
331 | 331 | "##### Discrete Data\n", |
332 | | - "Galton's observations include the number of **children** in each family. This is a *discrete* quantative data value - it's something we *count* rather than *measure*. You can't, for example, have 2.33 children!\n", |
| 332 | + "Galton's observations include the number of **children** in each family. This is a *discrete* quantitative data value - it's something we *count* rather than *measure*. You can't, for example, have 2.33 children!\n", |
333 | 333 | "##### Continuous Data\n", |
334 | | - "The data set also includes height values for **father**, **mother**, **midparentHeight**, and **childHeight**. These are measurements along a scale, and as such they're described as *continuous* quantative data values that we *measure* rather than *count*.\n", |
| 334 | + "The data set also includes height values for **father**, **mother**, **midparentHeight**, and **childHeight**. These are measurements along a scale, and as such they're described as *continuous* quantitative data values that we *measure* rather than *count*.\n", |
335 | 335 | "\n", |
336 | 336 | "### Sample vs Population\n", |
337 | 337 | "Galton's dataset includes 933 observations. It's safe to assume that this does not account for every person in the world, or even just the UK, in 1886 when the data was collected. In other words, Galton's data represents a *sample* of a larger *population*. It's worth pausing to think about this for a few seconds, because there are some implications for any conclusions we might draw from Galton's observations.\n", |
|
630 | 630 | }, |
631 | 631 | { |
632 | 632 | "cell_type": "code", |
633 | | - "execution_count": 62, |
| 633 | + "execution_count": 1, |
634 | 634 | "metadata": {}, |
635 | 635 | "outputs": [ |
636 | | - { |
637 | | - "data": { |
638 | | - "text/plain": [ |
639 | | - "Time Series:\n", |
640 | | - "Start = 1912 \n", |
641 | | - "End = 1971 \n", |
642 | | - "Frequency = 1 \n", |
643 | | - " [1] 49.9 52.3 49.4 51.1 49.4 47.9 49.8 50.9 49.3 51.9 50.8 49.6 49.3 50.6 48.4\n", |
644 | | - "[16] 50.7 50.9 50.6 51.5 52.8 51.8 51.1 49.8 50.2 50.4 51.6 51.8 50.9 48.8 51.7\n", |
645 | | - "[31] 51.0 50.6 51.7 51.5 52.1 51.3 51.0 54.0 51.4 52.7 53.1 54.6 52.0 52.0 50.9\n", |
646 | | - "[46] 52.6 50.2 52.6 51.6 51.9 50.5 50.9 51.7 51.4 51.7 50.8 51.9 51.8 51.9 53.0" |
647 | | - ] |
648 | | - }, |
649 | | - "metadata": {}, |
650 | | - "output_type": "display_data" |
651 | | - }, |
652 | 636 | { |
653 | 637 | "data": { |
654 | 638 | "image/png": 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|
|
673 | 657 | "source": [ |
674 | 658 | "The line chart shows the temperature trend from left to right for the period of observations. From this chart, you can see that the average temperature fluctuates from year to year, but the general trend shows an increase between the 1940s and 1950s." |
675 | 659 | ] |
| 660 | + }, |
| 661 | + { |
| 662 | + "cell_type": "code", |
| 663 | + "execution_count": null, |
| 664 | + "metadata": {}, |
| 665 | + "outputs": [], |
| 666 | + "source": [] |
676 | 667 | } |
677 | 668 | ], |
678 | 669 | "metadata": { |
|
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