Lectures: Stat 202 Fall 2016 - Revision history

Carver: /* September 15, 2016 */

2016-09-17T16:26:49Z

‎September 15, 2016

Carver at 16:24, 17 September 2016

2016-09-17T16:24:05Z

Carver: /* September 14, 2016 */

2016-09-15T14:11:54Z

‎September 14, 2016

Carver: /* September 14, 2016 */

2016-09-15T13:58:59Z

‎September 14, 2016

Carver at 13:55, 15 September 2016

2016-09-15T13:55:36Z

Carver: /* September 12, 2016 */

2016-09-14T13:49:53Z

‎September 12, 2016

Carver: /* September 12, 2016 */

2016-09-14T13:38:58Z

‎September 12, 2016

Carver: /* September 12, 2016 */

2016-09-14T13:38:11Z

‎September 12, 2016

Carver: /* September 8, 2016 */

2016-09-14T13:36:47Z

‎September 8, 2016

Carver: /* September 8, 2016 */

2016-09-08T23:39:30Z

‎September 8, 2016

← Older revision		Revision as of 16:26, 17 September 2016
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	* Homework: I handed out [[Media:Stat202_2015S_HW6.pdf\|Homework 6]]. The due date will be discussed during next Monday's class.		* Homework: I handed out [[Media:Stat202_2015S_HW6.pdf\|Homework 6]]. The due date will be discussed during next Monday's class.
	* Homework: I handed out [[Media:Stat202_2015S_HW7.pdf\|Homework 7]]. The due date will be discussed during next Monday's class.		* Homework: I handed out [[Media:Stat202_2015S_HW7.pdf\|Homework 7]]. The due date will be discussed during next Monday's class.
		+	* Next class: we will discuss QQ plots, hand out Homework 8, and I expect to have review materials prepared to hand out.

← Older revision		Revision as of 16:24, 17 September 2016
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	* Today we reviewed standard deviation, transformations, density curves, the relationship between density curves and histograms. We discussed the fact that all density curves describe a distribution, but to be a density curve: necessarily the area under the curve must be 1 and whole density curve must be on or above the vertical axis. We talked about bell curves and the relationship between bell curves and the normal distribution (bell curves are the density curves for normal distributions). Bell curves have a very specific shape that depends on only two parameters: mean and standard deviation. Knowing mean and standard deviation you can write down an exact mathematical formula for the bell curve. If mean is mu and standard deviation is sigma, the unique normal distribution is denoted N(mu,sigma). We talked about the mean, median, and mode of a density curve: for a bell curve these three things coincide at the peak. At one standard deviation from the mean of a bell curve you will find the inflection points where the curve goes from smiling to frowning or vice-versa (remember: bell curve means normal distribution, this doesn't work for other distributions). Another property of a normal distribution: if you do a linear transformation and the old variable is normal, the new one is normal, too. If the new variable is normal then the old variable must have also been normal. Another property of normal distribution is the 68-95-99.7 Rule, the percentage of area falling one, two, or three standard deviations from the mean. We talked about pseudo-random numbers, simulating pseudo-random number in StatCrunch, and the concept of seed, including fixed seed and dynamic seed. We talked about standardizing observations and the z-score.		* Today we reviewed standard deviation, transformations, density curves, the relationship between density curves and histograms. We discussed the fact that all density curves describe a distribution, but to be a density curve: necessarily the area under the curve must be 1 and whole density curve must be on or above the vertical axis. We talked about bell curves and the relationship between bell curves and the normal distribution (bell curves are the density curves for normal distributions). Bell curves have a very specific shape that depends on only two parameters: mean and standard deviation. Knowing mean and standard deviation you can write down an exact mathematical formula for the bell curve. If mean is mu and standard deviation is sigma, the unique normal distribution is denoted N(mu,sigma). We talked about the mean, median, and mode of a density curve: for a bell curve these three things coincide at the peak. At one standard deviation from the mean of a bell curve you will find the inflection points where the curve goes from smiling to frowning or vice-versa (remember: bell curve means normal distribution, this doesn't work for other distributions). Another property of a normal distribution: if you do a linear transformation and the old variable is normal, the new one is normal, too. If the new variable is normal then the old variable must have also been normal. Another property of normal distribution is the 68-95-99.7 Rule, the percentage of area falling one, two, or three standard deviations from the mean. We talked about pseudo-random numbers, simulating pseudo-random number in StatCrunch, and the concept of seed, including fixed seed and dynamic seed. We talked about standardizing observations and the z-score.
	* Next class: We will review all of the above, introduce QQ plots (also called normal quantile plots), and work on homeworks 6, 7, 8. This will complete our coverage of chapter 1, and complete our coverage of the material on exam 1. Next week we will review, and may start new material. Our exam will be Thursday September 22, 2016.		* Next class: We will review all of the above, introduce QQ plots (also called normal quantile plots), and work on homeworks 6, 7, 8. This will complete our coverage of chapter 1, and complete our coverage of the material on exam 1. Next week we will review, and may start new material. Our exam will be Thursday September 22, 2016.
		+
		+	== September 15, 2016 ==
		+
		+	* Today, we spent the class time working on homework problems concerning the material we covered during the previous class.
		+	* Homework: I handed out [[Media:Stat202_2015S_HW6.pdf\|Homework 6]]. The due date will be discussed during next Monday's class.
		+	* Homework: I handed out [[Media:Stat202_2015S_HW7.pdf\|Homework 7]]. The due date will be discussed during next Monday's class.

@@ Line 48: / Line 48: @@
 == September 14, 2016 ==
-* Today we reviewed standard deviation, transformations, density curves, the relationship between density curves and histograms.  We discussed the fact that all density curves describe a distribution, but to be a density curve: necessarily the area under the curve must be 1 and whole density curve must be on or above the vertical axis.  We talked about bell curves and the relationship between bell curves and the normal distribution (bell curves are the density curves for normal distributions).  Bell curves have a very specific shape that depends on only two parameters: mean and standard deviation.  Knowing mean and standard deviation you can write down an exact mathematical formula for the bell curve.  If mean is mu and standard deviation is sigma, the unique normal distribution is denoted N(mu,sigma).  We talked about the mean, median, and mode of a density curve: for a bell curve these three things coincide at the peak.  At one standard deviation from the mean of a bell curve you will find the inflection points where the curve goes from smiling to frowning or vice-versa (remember: bell curve means normal distribution, this doesn't work for other distributions).  Another property of a normal distribution: if you do a linear transformation and the old variable is normal, the new one is normal, too.  If the new variable is normal then the old variable must have also been normal.  Another property of normal distribution is the 68-95-99.7 Rule, the percentage of area falling one, two, or three standard deviations from the mean.  We talked about pseudo-random numbers, simulating pseudo-random number in StatCrunch, and the concept of seed, including fixed seed and dynamic seed.
+* Today we reviewed standard deviation, transformations, density curves, the relationship between density curves and histograms.  We discussed the fact that all density curves describe a distribution, but to be a density curve: necessarily the area under the curve must be 1 and whole density curve must be on or above the vertical axis.  We talked about bell curves and the relationship between bell curves and the normal distribution (bell curves are the density curves for normal distributions).  Bell curves have a very specific shape that depends on only two parameters: mean and standard deviation.  Knowing mean and standard deviation you can write down an exact mathematical formula for the bell curve.  If mean is mu and standard deviation is sigma, the unique normal distribution is denoted N(mu,sigma).  We talked about the mean, median, and mode of a density curve: for a bell curve these three things coincide at the peak.  At one standard deviation from the mean of a bell curve you will find the inflection points where the curve goes from smiling to frowning or vice-versa (remember: bell curve means normal distribution, this doesn't work for other distributions).  Another property of a normal distribution: if you do a linear transformation and the old variable is normal, the new one is normal, too.  If the new variable is normal then the old variable must have also been normal.  Another property of normal distribution is the 68-95-99.7 Rule, the percentage of area falling one, two, or three standard deviations from the mean.  We talked about pseudo-random numbers, simulating pseudo-random number in StatCrunch, and the concept of seed, including fixed seed and dynamic seed.  We talked about standardizing observations and the z-score.
 * Next class: We will review all of the above, introduce QQ plots (also called normal quantile plots), and work on homeworks 6, 7, 8.  This will complete our coverage of chapter 1, and complete our coverage of the material on exam 1.  Next week we will review, and may start new material.  Our exam will be Thursday September 22, 2016.

@@ Line 48: / Line 48: @@
 == September 14, 2016 ==
-* Today we reviewed standard deviation, transformations, density curves, the relationship between density curves and histograms, the fact that all density curves describe a distribution, but necessarily the area under a density curve must be 1 and whole density curve must be on or above the vertical axis.  We talked about bell curves and the relationship between bell curves and the normal distribution (bell curves are the density curves for normal distributions).  Bell curves have a very specific shape that depends on only two parameters: mean and standard deviation.  Knowing mean and standard deviation you can write down an exact mathematical formula for the bell curve.  If mean is mu and standard deviation is sigma, the unique normal distribution is denoted N(mu,sigma).  We talked about the mean, median, and mode of a density curve.  For a bell curve these three things coincide at the peak.  At one standard deviation from the mean of a bell curve you will find the inflection points where the curve goes from smiling to frowning or vice-versa (remember: bell curve means normal distribution, this doesn't work for other distributions).  Another property of a normal distribution: if you do a linear transformation and the old variable is normal, the new one is normal, too.  If the new variable is normal then the old variable must have also been normal.  Another property of normal distribution is the 68-95-99.7 Rule, the percentage of area falling one, two, or three standard deviations from the mean.  We talked about pseudo-random numbers, simulating pseudo-random number in StatCrunch, and the concept of seed, including fixed seed and dynamic seed.
+* Today we reviewed standard deviation, transformations, density curves, the relationship between density curves and histograms.  We discussed the fact that all density curves describe a distribution, but to be a density curve: necessarily the area under the curve must be 1 and whole density curve must be on or above the vertical axis.  We talked about bell curves and the relationship between bell curves and the normal distribution (bell curves are the density curves for normal distributions).  Bell curves have a very specific shape that depends on only two parameters: mean and standard deviation.  Knowing mean and standard deviation you can write down an exact mathematical formula for the bell curve.  If mean is mu and standard deviation is sigma, the unique normal distribution is denoted N(mu,sigma).  We talked about the mean, median, and mode of a density curve: for a bell curve these three things coincide at the peak.  At one standard deviation from the mean of a bell curve you will find the inflection points where the curve goes from smiling to frowning or vice-versa (remember: bell curve means normal distribution, this doesn't work for other distributions).  Another property of a normal distribution: if you do a linear transformation and the old variable is normal, the new one is normal, too.  If the new variable is normal then the old variable must have also been normal.  Another property of normal distribution is the 68-95-99.7 Rule, the percentage of area falling one, two, or three standard deviations from the mean.  We talked about pseudo-random numbers, simulating pseudo-random number in StatCrunch, and the concept of seed, including fixed seed and dynamic seed.
 * Next class: We will review all of the above, introduce QQ plots (also called normal quantile plots), and work on homeworks 6, 7, 8.  This will complete our coverage of chapter 1, and complete our coverage of the material on exam 1.  Next week we will review, and may start new material.  Our exam will be Thursday September 22, 2016.

@@ Line 40: / Line 40: @@
 == September 12, 2016 ==
 * Today, we quickly reviewed measures of center and spread, 5 number summary, box plot and modified box plot.  We covered transformations.  We covered standard deviation.  We covered ''Sampling'' in [[Media:The_Data_Professors_Guide_to_Basic_Statistics.pdf|The Data Professor's Guide to Basic Statistics]].  We also discussed the definition of the number "N choose n."
@@ Line 46: / Line 45: @@
 * Homework: [[Media:Stat202_2015S_HW5.pdf|Homework 5]] was assigned today with a due date of September 21, 2016.
 * Next class: We will discuss the definition of the number "N choose n."  We will discuss density curves and start probability.

← Older revision		Revision as of 13:38, 14 September 2016
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	== September 12, 2016 ==		== September 12, 2016 ==
		+
		+
		+	* Today, we quickly reviewed measures of center and spread, 5 number summary, box plot and modified box plot. We covered transformations. We covered standard deviation. We covered ''Sampling'' in [[Media:The_Data_Professors_Guide_to_Basic_Statistics.pdf\|The Data Professor's Guide to Basic Statistics]]. We also discussed the definition of the number "N choose n."
		+	* Homework: [[Media:Stat202_2015S_HW5.pdf\|Homework 5]] was assigned today with a due date of September 21, 2016.
		+	* Next class: We will discuss the definition of the number "N choose n." We will discuss density curves and start probability.

← Older revision		Revision as of 13:36, 14 September 2016
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	* Reading: Moore, McCabe & Craig, pp. 37-42.		* Reading: Moore, McCabe & Craig, pp. 37-42.
	* Next class: We will quickly review measures of center and spread, 5 number summary, box plot and modified box plot. We will cover transformations. We will have class time for Homeworks 4 and 5. We will cover standard deviation and start to study sampling.		* Next class: We will quickly review measures of center and spread, 5 number summary, box plot and modified box plot. We will cover transformations. We will have class time for Homeworks 4 and 5. We will cover standard deviation and start to study sampling.
		+
		+	== September 12, 2016 ==

← Older revision		Revision as of 23:39, 8 September 2016
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	* Practice Problems Solutions: [[Media:Stat20X_Week01_Practice_Solutions.pdf\|Practice Problem Solutions for Week 1]].		* Practice Problems Solutions: [[Media:Stat20X_Week01_Practice_Solutions.pdf\|Practice Problem Solutions for Week 1]].
	* Reading: Moore, McCabe & Craig, pp. 37-42.		* Reading: Moore, McCabe & Craig, pp. 37-42.
		+	* Next class: We will quickly review measures of center and spread, 5 number summary, box plot and modified box plot. We will cover transformations. We will have class time for Homeworks 4 and 5. We will cover standard deviation and start to study sampling.