Question

For this activity, select a recurring quantity from your OWN life for which you have monthly records at least 2 years (including 24 observation in dataset at least). This might be the cost of a utility bill, the number of cell phone minutes used, or even your income. If you do not have access to such records, use the internet to find similar data, such as average monthly housing prices, rent prices in your area for at least 2 years (You must note the data source with an accessible link). Data can also be monthly sales of some particular commodity. 1.4 Please do the descriptive analysis, using the method of index number and Exponential Smoothing individually. And try to explain the pattern you find. 1.5 Use two methods you learned to predict the value of your quantity for the next year (12 months). And make comparison with two results.
MONTHS INTERNET BILL

Jan(2018) 1352

Feb    1434

March    1473

   April 1879
May 3373

June    2249

   July    1327

   August    1536

   September 1810

October 2060

November 3494

December 2399

   Jan(2019) 1410

Feb    1685

March    1724

   April 2223

   May 3794

June 2662

   July 1537

August    1824

September    1888

October    2264

November    3895

December    3124

Answer 1

The Exponential Smoothing method is:

Alpha	0.3
Data			Forecasts and Error Analysis
Period	Demand		Forecast	Error	Absolute	Squared	Abs Pct Err
Period 1	1352		1352	0	0	0	00.00%
Period 2	1434		1352	82	82	6724	05.72%
Period 3	1473		1376.6	96.4	96.4	9292.96	06.54%
Period 4	1879		1405.52	473.48	473.48	224183.3	25.20%
Period 5	3373		1547.564	1825.436	1825.436	3332217	54.12%
Period 6	2249		2095.195	153.8052	153.8052	23656.04	06.84%
Period 7	1327		2141.336	-814.336	814.3364	663143.7	61.37%
Period 8	1536		1897.035	-361.035	361.0355	130346.6	23.50%
Period 9	1810		1788.725	21.27518	21.27518	452.6334	01.18%
Period 10	2060		1795.107	264.8926	264.8926	70168.1	12.86%
Period 11	3494		1874.575	1619.425	1619.425	2622537	46.35%
Period 12	2399		2360.403	38.59739	38.59739	1489.758	01.61%
Period 13	1410		2371.982	-961.982	961.9818	925409	68.23%
Period 14	1685		2083.387	-398.387	398.3873	158712.4	23.64%
Period 15	1724		1963.871	-239.871	239.8711	57538.14	13.91%
Period 16	2223		1891.91	331.0902	331.0902	109620.7	14.89%
Period 17	3794		1991.237	1802.763	1802.763	3249955	47.52%
Period 18	2662		2532.066	129.9342	129.9342	16882.9	04.88%
Period 19	1537		2571.046	-1034.05	1034.046	1069251	67.28%
Period 20	1824		2260.832	-436.832	436.8322	190822.4	23.95%
Period 21	1888		2129.783	-241.783	241.7826	58458.81	12.81%
Period 22	2264		2057.248	206.7522	206.7522	42746.47	09.13%
Period 23	3895		2119.273	1775.727	1775.727	3153205	45.59%
Period 24	3124		2651.991	472.0086	472.0086	222792.1	0.1510911
			Total	4805.313	13781.86	16339605	592.22%
			Average	200.2214	574.2441	680816.9	24.68%
				Bias	MAD	MSE	MAPE
					SE	861.8058
Next period	2793.59399

The regression method is:

	r²	0.163
	r	0.404
	Std. Error	747.260
	n	24
	k	1
	Dep. Var.	Data
ANOVA table
Source	SS	df	MS	F	p-value
Regression	23,93,544.5435	1	23,93,544.5435	4.29	.0504
Residual	1,22,84,729.4565	22	5,58,396.7935
Total	1,46,78,274.0000	23
Regression output				confidence interval
variables	coefficients	std. error	t (df=22)	p-value	95% lower	95% upper
Intercept	1,613.7283
t	45.6217	22.0355	2.070	.0504	-0.0771	91.3205
Predicted values for: Data
t	Predicted
25	2,754.272
26	2,799.893
27	2,845.515
28	2,891.137
29	2,936.759
30	2,982.380
31	3,028.002
32	3,073.624
33	3,119.246
34	3,164.867
35	3,210.489
36	3,256.111

The regression method is better because it has lower MSE value.