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cluponka [151]
3 years ago
13

Luminous objects such as flashlights emit light that we can see. How do we see an object that isn’t luminous?

SAT
2 answers:
KonstantinChe [14]3 years ago
5 0

The object which are not luminous  are observed by the light reflected off the object and reaches our eyes.

Option B

<h3><u>Explanation:</u></h3>

A non-luminous object reflects light from its surface that is incident on it from some other luminous source or body, to be seen by the naked eye as a whole. The light reflected, reaches the observer’s eyes and is viewed as a result.

Non-luminous objects never emit their light, rather they are dependent on luminous objects to be seen by the observer. A piece of cloth, moon, earth, wood, plastics, metals, etc. are some fundamental examples of a non-luminous object.

o-na [289]3 years ago
3 0

B is the correct answer.

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Find the residual values, and use the graphing calculator tool to make a residual plot.
finlep [7]

Answer:

  • No, the points are evenly distributed about the x-axis.

Explanation:

<u>1. Write the table with the data:</u>

  x      given     predicted       residual

  1       - 3.5         - 1.1

  2      - 2.9           2

  3       - 1.1            5.1

  4        2.2           8.2

  5        3.4           1.3

<u>2. Complete the column of residuals</u>

The residual is the observed (given) value - the predicted value.

  • residual = given - predicted.

Thus, the complete table, with the residual values are:

  x      given     predicted       residual

  1       - 3.5         - 1.1                 - 2.4

  2      - 2.9           2                  - 4.9

  3       - 1.1            5.1                - 6.2

  4        2.2           8.2               - 6.0

  5        3.4           1.3                   2.1

<u>3. Residual plot</u>

You must plot the last column:

  x      residual

  1       - 2.4

  2      - 4.9

  3       - 6.2

  4       - 6.0

  5        2.1

See the plot attached.

<em>Does the residual plot show that the line of best fit is appropriate for the data?</em>

Ideally, a residual plot for a line of best fit that is appropiate for the data must not show any pattern; the points should be randomly distributed about the x-axis.

But the points of the plot are not randomly distributed about the x-axis:  there are 4 points below the x-axis and 1 point over the x-axis: there are more negative residuals than positive residuals.  This is a pattern. Also, you could say that they show a curve pattern, which drives to the same conclusion: the residual plot shows that the line of best fit is not appropiate for the data.

Thus, the conclusion should be: No, the points have a pattern.

  • 1. "<em>Yes, the points have no pattern</em>": false, because as shown, the points do have a pattern, which makes the residual plots does not show that the line of best fit is appropiate for the data.

  • 2. "<em>No, the points are evenly distributed about the x-axis</em>": true. As already said the points have a pattern. It is a curved pattern, and this <em>shows the line of best fit is not appropiate for the data.</em>

  • 3. "<em>No, the points are in a linear pattern</em>": false. The points are not in a linear pattern.

  • 4. "<em>Yes, the points are in a curved pattern</em>": false. Because the points are in a curved pattern, the residual plot shows that the line of best fit is not appropiate for the data.

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Because Stalin feared Western capitalist expansion, he refused to allow free elections in Eastern Europe.

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Hence, we can see that based on the difference in ideology between America and the Soviet Union; capitalism and communism, Joseph Stalin refused to allow free elections in Eastern Europe for a fear of capitalist expansion.

Read more about Joseph Stalin here:

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