ECON 120A Lecture Notes - Lecture 2: Percentile, Quartile, Unimodality

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brownyak123

8 Oct 2016

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Descriptive statistics (ch 2: discrete example x = # of children in a family x. Sum f (frequency = # of times we see a value in our data) Relative frequency = % of times that a value of x occurs in our data = f/n. Measure of center / location: mode. Most frequent value e. g. mode = 2. Unimodal = 1 mode bimodal = 2 mode. Multimodal = > 2 mode: median. 90th percentile = 90% of observation are equal or lower lower quartile = 1st quartile = q1 = ql = 25th percentile upper quartile = 3rd quartile = q3 = qu = 75th percentile. Method to compute quartile use median to separate your ordered data into halves.

Related Questions

PROBLEM SET ONE -2 - PRICE-ELASTICITY OF DEMAND

	P₁	P₂	Q_D1	Q_D2	I	II	III	IV	V
1	1	2	10	5
2	5	3	40	90
3	12	20	200	200
4	3	2	9	9
5	0.40	1.40	30	15
6	1.2	4.0	20	15
7	7.5	4.6	40	30
8	5	5.000â¦01	1x10⁶	0
9	5	4.9999â¦.	10	1x10⁹
10	6	12	12	6
11	18	36	360	180
12	7	15	24	16
13	7	16	78	78
14	25	65	150	100
15	65	91	1300	780
16	4	5	21	11
17	8	4	30	54
18	140	275	625	495
19	78	91	780	780
20	91	78	780	780

Column I - determine the Price-Elasticity of Demand Coefficient. Find material in Chapter 6 and the Powerpoint of Elasticity Chap 004 19e.ppt

change in quantity demanded change in price

E_D = ---------------------------------------- â ----------------------------

sum of quantities demanded / 2 sum of prices / 2

The data in the first four columns represent price (P) and quantity demanded (Qd) in time 1 (before change in price) and time 2 (after change in price). Note that results should be expressed in absolute terms (see paragraph Elimination of Minus Sign). For example, -1 should be expressed as â1â.

Column II â Based on topic concerning Interpretations of E_D, indicate which applies based on how the quantity demanded changed subsequent to a change in price (elastic, inelastic, etc.)

Column III - you need to determine if the good in question would be considered a necessity, a luxury or neither.

Column IV â Indicate, in monetary terms, how much is the change in total revenue or total expenditure (TR = P X Q_D), from the first price level to the second.

Column V - the direction of the change, also in monetary terms, that is, increasing or decreasing, and by how much? (show a + sign for increasing and a â sign for decreasing).

Note: for any monetary result please include the applicable currency symbol ($, â¬, etc.)

salmoneagle868

The data for this assignment comes from household surveys carried out on a small island in Eastern Indonisa. The island, Selayar Island, is located in South Sulawesi, has two main industries: fishing and agriculture. We will be focusing of fishing activities occurring nearshore. Nearshore fishing takes place over coral reef, seagrass, and mangrove habitat. The most common fishing gear include spear guns, pole and lines, and nets. The boats used for fishing range from small canoes to small boats. The data was collected using a randomn sampling technique, and includes 152 fishing households.

Each fishing household was allowed to describe up to 6 different types of fishing trips. Researchers collected information on the âaverge tripâ. A subset of the data has been provided, in a .csv file named: âPS3_NearshoreFishing.csvâ.

Description of variables:

HouseholdID: Unique identification number used to protect the identity of respondents

Poor: Indicates poor and non-poor households, as defined by the Indonesian National Poverty Line ($0.83 per capita per day).

Poor = 1 if household income is less than the INPL

= 0 if household income is more than the INPL

Quantity: The quantity of average harvest during one trip, in 10,000 Rp (currency)

For example, if average trip harvest is 2 kg of Grouper, and Grouper price is 20,000 Rp/kg, then Quantity of the trip is 40

Hours: The number of person hours spent fishing per trip, in hours.

For example, if average trip includes 3 fishermen, and each fishermen spends 3 hours fishing, then Hours of the trip is 9 person hours.

Capital: The value of capital used up during the average trip, in 10,000 Rp.

You can think of this like depreciation of all gear and boats used during a trip

Part 1:

(Model 1)

log(Quantityi)= b0 + b1log(Capitali) + b2log(Hoursi) + ei

1. Estimate the basic production model and report regression results. Include coefficients, standard errors, and t-statics. (Note: You will have to create new variables for estimation)

2. Based on your results, what is the marginal reurns to hours spent fishing? Be careful to interpret coefficient on log-transformed variables correctly.

3. Does the estimated production function have constant returns to scale, decreasing returns to scale, or increasing returns to scale? How do you know?

4. Using single hypothesis tests, which coefficients are significantly different than zero at a 5% significiance level?

5. Use Whiteâs Test to test for heteroskedasticity. Report results and evaluate the results.

Help with the following problems that deal with the program stata and with econometrics.

Below is the data needed.

HouseholdID	Poor	Quantity	Hours	Capital
125465	0	25.4	8	0.796489877
130552	0	28.6	6	1.174603175
130552	0	9.1	4	2.222222222
127524	0	5	1	0.36544481
130538	1	10	6	2.008032129
130558	1	18.75	28	14.17848124
130462	1	14.4	2	0.487684729
126466	0	14.1	6	4.619435291
127474	1	13	3	0.367063492
127498	1	6	3	0.568124297
127471	0	12	5	0.115421137
127493	1	2.1	8	0.123444444
130561	0	39.6	3	0.289115646
130562	0	8	6	1.554278416
127532	1	41.7	6	0.738153469
127532	1	62.9	6	4.832562392
127495	0	7.65	3.5	0.116631206
127495	0	11.6	4	0.148772937
130524	1	2.95	10	0.706882255
130524	1	2.95	10	0.706882255
130564	0	14	14	1.495345632
127505	0	21.6	4	1.252053872
135460	1	21.5	4	0.757665094
131482	0	42.5	10	1.959625576
130560	0	15.3	4	0.697751323
128510	0	6	3	0.002311604
128510	0	4	2.5	0.416666667
128480	1	3.9	10	1.237449199
128480	1	4.4	10	1.237449199
128480	1	3.5	6	0.722079449
132456	0	8.5	5	0.177372685
132456	0	16.25	3	0.106423611
135486	0	21.5	11	3.665322581
135486	0	52.5	6	0.836021505
134524	0	33	7	1.242139722
135488	0	19.8	5	1.217105263
134460	0	84.4	3	3.824833703
131479	0	4	4.5	0.061111111
131465	0	8.5	1.5	0.39040198
128491	0	3.8	6	2.53815261
141514	1	1.4	6	0.946825397
134484	0	151.2	7	0.552428601
134484	0	71.2	6	0.479205754
141459	0	6.7	4	1.955605159
128473	0	70.7	5.5	0.553802416
128475	0	14.5	4	0.807217474
135520	0	2.5	4	1.041666667
135512	0	13.1	1	5.561594203
135512	0	3.3	2	0.650164062
134513	0	16.4	1	3.680053548
135469	1	18	13	1.979166667
132520	1	5.0625	2	0.395197395
134523	1	32.2	7	0.684749501
134497	0	10.76	5	3.18869165
129478	0	45.5	5.5	2.511574074
141461	0	25.6	2.5	4.849261849
141461	0	36.6	2.5	0.599912165
134503	0	30.6	8	1.214055483
128506	0	13.8	4	9.222222222
128506	0	19	4	9.222222222
130563	0	183.5	4	27.46449975
129488	0	215.1	1134	168.6111111
128487	0	4.5	1	0.000559284
129461	0	244	360	35.33333333
128496	0	1150	504	475.7380952
173468	0	2455	3366	1729.189322
134499	0	199	4	1.204772678
129466	0	4.5	3	2.773530545
145508	1	15	4	0.01460387
145508	1	15	4	0.01460387
134488	0	175	7	2.463414634
129484	0	275.1	2592	201.2685185
129484	0	175.1	1134	167.9891975
141467	0	16.5	2.5	0.270061728
141457	0	8.1	3	0.12021137
141457	0	24.1	4	0.499612792
135475	0	4.75	11	0.229402703
129498	0	616	830	362.7
134522	0	86.9	3	1.51324121
128477	0	9.4	5	1.626364087
141468	0	91.7	0.5	0.983115468
135489	1	18	8	3.206349206
134510	0	644.2	210	175.3837719
128457	0	3.5	2	0.509259259
134462	0	84.6	6	2.877641939
141509	1	23.2	2	2.640595716
129463	0	331	384	47.38180577
135514	1	18.6	2	8.338907469
135514	1	36.6	1	2.719893807
129485	0	4	7	0.833333333
129485	0	71.5	80	5.716746662
132529	1	7.8	3	1.024350649
132529	1	3.5	2	1.794733045
132529	1	1.5	3	0.861688312
145478	1	10.1	6	0.125
139510	1	12.5	10	6.041666667
134470	0	112	6	2.621527778
134496	0	279.7	4	4.799723021
134495	0	69	6	0.693735987
141460	0	60	2	1.272727273
135509	0	132.6	5	1.686077644
135506	0	10.5	3	0.295375197
134514	0	147.55	4	0.369502642
134514	0	147.55	4	0.369502642
134474	0	234.8	6	1.269918699
133503	0	6	3	1.095290252
133503	0	5.6	3	0.111111111
128460	0	15.5	5	0.306275676
128460	0	11.5	4	0.312922038
129501	0	500.5	483	74.36528749
129479	1	91	85	10.75104488
123490	1	15.75	5	1.35
123463	0	35.5	6	0.5
125465	0	18.5	6	0.568024685
127468	0	5.5	3.5	0.111111111
130552	0	13.1	2	1.166666667
130514	0	6.5	4	0.680555556
127524	0	14.5	5	2.532393505
130558	1	28	32	14.18542569
130516	0	3.5	4	0.208333333
130462	1	6.8	3	0.674055829
126466	0	12.6	2	4.592549518
127485	0	4.2	12	2.191666667
127485	0	10.2	6	1.186111111
130457	0	15	8	7.291666667
127474	1	10.5	3	0.724206349
127498	1	4.25	3	0.053412073
127498	1	3.5	3	0.053412073
127471	0	17.1	8	4.690142913
127471	0	12.3	4	2.311659648
127471	0	2.5	2	1.666666667
127493	1	2.1	8	0.123444444
130561	0	38.7	3	0.289115646
130561	0	16.5	3	11.33333333
127478	0	119.5	10	3.857877016
127478	0	30.5	6	2.170875421
127532	1	42.7	6	0.622768853
127532	1	38.7	6	6.971699297
127467	0	4.5	4	0.499600497
127467	0	9.1	3.5	0.366892361
127495	0	3.1	3.5	0.110797872
127495	0	9.9	3	0.114859845
130524	1	2.45	10	0.706882255
130524	1	2.45	10	0.706882255
130564	0	3814	3332	854.4818928
127505	0	14.5	3.5	2.491380471
135460	1	10	4	0.757665094
135460	1	20	14	0.484052111
131482	0	23.6	5	0.360957057
131482	0	67.9	6	0.416712483
130560	0	14	3	0.922110297
128510	0	504.6	1440	228.8873478
128510	0	6.3	2.5	0.416666667
128480	1	3.5	8	0.928789147
128480	1	49	70	33.6678911
132456	0	14.5	5	0.474991733
132456	0	8.75	5	0.177372685
132531	0	1.75	7	0.333333333
134524	0	33	7	1.116770991
134524	0	17	8	1.318727966
132513	0	1.5625	5	0.083333333
133494	0	2.875	2.5	0.5
135488	0	19.8	5	1.217105263
134460	0	40.8	3	3.824833703
128509	0	4216	2880	975.4285714
131479	0	2.35	4.5	0.42333225
131465	0	73	11.5	3.398356695
128491	0	6.4	3	1.871485944
141514	1	7.75	6	0.946825397
134484	0	91.7	7	0.962685011
134484	0	141.2	6	0.479205754
141459	0	12.2	4	1.955605159
128473	0	31.8	11	5.15297619
128481	0	852	810	49.09259259
128475	0	15.4	4	0.807217474
140461	1	4.7	5	1.183343855
128476	0	47	14	1.406476684
144544	0	21.2	2	0.866714015
135496	0	157.5	5	2.321399233
135496	0	59.8	5	2.003217415
134513	0	15.4	1	3.680053548
134523	1	28	40	3.667270345
144562	0	18.5	3	3.87254902
144562	0	11.4	3	2.086834734
134497	0	16.4	4	2.740795529
141461	0	34.1	2.5	4.849261849
141461	0	91.1	10	2.327184892
134503	0	29.6	8	1.214055483
128506	0	1271	1152	801
135497	0	0.75	1	0.221153846
141495	0	29	5	1.555555556
130563	0	252.5	8	36.62834591
141505	0	0.8	3	0.488855699
141505	0	0.8	3	0.513748468
133497	0	10	3	0.011904762
129488	0	210.5	1134	168.6111111
129461	0	722	720	70.66666667
128496	0	1150	504	475.7380952
173468	0	2882.5	1600	786.019471
145508	1	18.5	5	0.018254838
145508	1	16.65	5	0.018254838
134488	0	35	7	2.463414634
129484	0	371	2430	188.7268519
141467	0	25.65	2	1.158035714
141467	0	12.15	6.5	2.02808642
141457	0	19.8	3	0.167764213
141457	0	14.1	2	0.267971028
135475	0	5.75	10.5	0.22358875
129498	0	1068	830	362.7
134522	0	42.4	4	1.889213987
128477	0	7.6	6	1.919229497
144504	0	20.4	6	1.648461358
141468	0	35.65	0.5	0.983115468
135489	1	17.8	8	3.206349206
134510	0	384.7	90	75.16447368
128457	0	10	2	0.509259259
134462	0	114.6	7	2.918652438
141509	1	7.9	2	5.150635877
131485	0	6.75	8	2.973157134
131485	0	6.5	9	3.271105631
129463	0	1977.4	1848	224.9850657
135514	1	121.2	10	7.021349862
129485	0	1815	1296	80.22182224
132529	1	2.35	2.5	2.223124098
132529	1	7.6	2	1.875901876
132529	1	8.5	7	1.577705628
135502	0	31.2	4	0.320555556
132508	0	3.125	2.5	0.083333333
133479	0	26.7	2	4.126984127
134470	0	96	6	0.520833333
134496	0	114.8	7	2.416368587
134495	0	43	9	0.63703001
141460	0	23	2	1.272727273
132498	0	8	2	5.555555556
129497	0	2206	2880	955.3703704
129497	0	9.1	1.5	3.888888889
135509	0	2.6	5	0.942994199
135506	0	23.75	3	0.318339994
134514	0	37.55	7	0.596927834
134514	0	37.55	5	0.445311039
134474	0	114.75	6	1.269918699
133503	0	9	4	1.460387003
133503	0	6	3	0.111111111
128460	0	13.5	5	0.306275676
144502	0	6	3	0.644824673
129501	0	1280.2	672	123.9083139
129479	1	112	833	100.6137228
129511	0	231	60	0.02

Mulberry Realty sells homes in the northeast section of the United States. A frequently asked question by prospective buyers is: If we buy this house, how much can we expect to pay to heat it during the winter? The research division of Mulberry is asked to develop some guidelines for heating costs (Cost) (measured in $) for single family homes. The division decides three variables are related to heating costs: (1) the mean daily outside temperature (Temp) (in degrees Fahrenheit); (2) the number of inches of attic insulation (Insulation); and (3) the age of the furnace in the house (Age) (measured in years). The division collected data during the month of January for a sample of homes. Parentheses contain the variable names (in bold) as listed in the data set.

The data set for this scenario is included in an Excel file.

a. Is the information used in this analysis, cross-sectional data or time series data? (4 pts)

b. State your a priori hypothesis about the sign of the slope for each independent variable. (3 pts each)

c. Write out the estimated regression equation using variable specific names? (5 pts)

d. Interpret the slope coefficient for the variable with the largest slope coefficient. (5 pts)

e. For the slope coefficient with the smallest value (in absolute value), test to see if it has the directional relationship hypothesized in part (b). Alpha = 0.05. (15 pts)

Data Set:

Cost ($)	Temp (deg)	Insulation (ins.)	Age (yrs)
250	35	3	6
360	29	4	10
165	36	7	3
43	60	6	9
92	65	5	6
200	30	5	5
355	10	6	7
290	7	10	10
230	21	9	11
120	55	2	5
73	54	12	4
205	48	5	1
400	20	5	15
320	39	4	7
72	60	8	6
272	20	5	8
94	58	7	3
190	40	8	11
235	27	9	8
139	30	7	5

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ECON 120A Lecture Notes - Lecture 2: Percentile, Quartile, Unimodality

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