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QM1 Formula Sheets

Two clean, exam-ready cheat sheets for Quantitative Methods I — Math and Statistics — including the standard Normal (Z) table and the t-distribution critical values table. 100% free, no signup.

1 · Math Formulas Sheet

Line equations

Point–slope form
yy1=m(xx1)y - y_1 = m(x - x_1)
Two-point form
yy1=y2y1x2x1(xx1)y - y_1 = \frac{y_2 - y_1}{x_2 - x_1}\,(x - x_1)
Slope from two points
m=y2y1x2x1m = \frac{y_2 - y_1}{x_2 - x_1}

Quadratic formula

Roots of ax² + bx + c = 0
x=b±b24ac2ax = \frac{-b \pm \sqrt{b^2 - 4ac}}{2a}
Discriminant
Δ=b24ac\Delta = b^2 - 4ac

Inverse functions

Power
f(x)=xn    f1(x)=x1/nf(x) = x^n \;\Rightarrow\; f^{-1}(x) = x^{1/n}
Exponential / Logarithm
f(x)=ex    f1(x)=ln(x)f(x) = e^{x} \;\Rightarrow\; f^{-1}(x) = \ln(x)
General base
f(x)=ax    f1(x)=loga(x)f(x) = a^{x} \;\Rightarrow\; f^{-1}(x) = \log_{a}(x)

Important derivatives

Constant
ddxc=0\frac{d}{dx}\,c = 0
Power rule
ddxxn=nxn1\frac{d}{dx}\,x^{n} = n\,x^{n-1}
Exponential
ddxex=ex\frac{d}{dx}\,e^{x} = e^{x}
General exponential
ddxax=axlna\frac{d}{dx}\,a^{x} = a^{x}\ln a
Natural log
ddxln(x)=1x\frac{d}{dx}\,\ln(x) = \frac{1}{x}
General log
ddxloga(x)=1xlna\frac{d}{dx}\,\log_{a}(x) = \frac{1}{x\,\ln a}

Derivative rules

RuleFormula
Sum / Difference(f±g)=f±g(f \pm g)' = f' \pm g'
Constant multiple(cf)=cf(cf)' = c\,f'
Product rule(fg)=fg+fg(fg)' = f'g + fg'
Quotient rule(fg)=fgfgg2\left(\frac{f}{g}\right)' = \frac{f'g - fg'}{g^{2}}
Chain rule (composition)(f(g(x)))=f(g(x))g(x)\big(f(g(x))\big)' = f'(g(x))\,g'(x)

Relative rate of increase & elasticity

Relative rate of increase
RRI(x)=f(x)f(x)=ddxlnf(x)\text{RRI}(x) = \frac{f'(x)}{f(x)} = \frac{d}{dx}\ln f(x)
Elasticity
Ef(x)=xf(x)f(x)=dlnf(x)dlnxE_{f}(x) = \frac{x}{f(x)}\,f'(x) = \frac{d \ln f(x)}{d \ln x}

Interpretation: a 1% increase in xx changes f(x)f(x) by approximately Ef(x)E_f(x)%.

Second-order conditions (stationary points)

A stationary point satisfies f(x0)=0f'(x_0) = 0. Classify with the second derivative:

ConditionType
f(x0)>0f''(x_0) > 0Local minimum
f(x0)<0f''(x_0) < 0Local maximum
f(x0)=0f''(x_0) = 0Inconclusive — possible saddle / inflection point (check sign change of ff')

For multivariable functions: a critical point is a saddle when the Hessian has eigenvalues of mixed sign (fxxfyyfxy2<0f_{xx}f_{yy} - f_{xy}^{2} < 0).


2 · Stats Formulas & Tables Sheet

Descriptive statistics

Range
Range=maxmin\text{Range} = \max - \min
IQR
IQR=Q3Q1\text{IQR} = Q_3 - Q_1
Outlier rule (1.5 × IQR)
x<Q11.5IQR    or    x>Q3+1.5IQRx < Q_1 - 1.5\,\text{IQR} \;\;\text{or}\;\; x > Q_3 + 1.5\,\text{IQR}
Mean
yˉ=1ni=1nyi\bar{y} = \frac{1}{n}\sum_{i=1}^{n} y_i
Sample standard deviation
s=1n1i=1n(yiyˉ)2s = \sqrt{\frac{1}{n-1}\sum_{i=1}^{n}(y_i - \bar{y})^{2}}

Z-score

Model-based (population)
z=yμσz = \frac{y - \mu}{\sigma}
Data-based (sample)
z=yyˉsz = \frac{y - \bar{y}}{s}

Correlation & linear regression

Correlation coefficient
r=1n1(xixˉsx)(yiyˉsy)r = \frac{1}{n-1}\sum \left(\frac{x_i - \bar{x}}{s_x}\right)\left(\frac{y_i - \bar{y}}{s_y}\right)
Regression line
y^=b0+b1x\hat{y} = b_0 + b_1 x
Slope
b1=rsysxb_1 = r\,\frac{s_y}{s_x}
Intercept
b0=yˉb1xˉb_0 = \bar{y} - b_1\,\bar{x}

Probability rules

Complement
P(Ac)=1P(A)P(A^{c}) = 1 - P(A)
Addition (general)
P(AB)=P(A)+P(B)P(AB)P(A \cup B) = P(A) + P(B) - P(A \cap B)
Multiplication (general)
P(AB)=P(A)P(BA)P(A \cap B) = P(A)\,P(B \mid A)
Conditional
P(BA)=P(AB)P(A)P(B \mid A) = \frac{P(A \cap B)}{P(A)}
Independence
AB    P(AB)=P(A)P(B)A \perp B \;\Leftrightarrow\; P(A \cap B) = P(A)\,P(B)

Random variables

Expected value (discrete)
E(X)=μ=xxP(X=x)E(X) = \mu = \sum_{x} x\,P(X = x)
Variance
Var(X)=σ2=x(xμ)2P(X=x)\text{Var}(X) = \sigma^{2} = \sum_{x}(x - \mu)^{2}\,P(X = x)
Linear transform — mean
E(aX+b)=aE(X)+bE(aX + b) = a\,E(X) + b
Linear transform — variance
Var(aX+b)=a2Var(X)\text{Var}(aX + b) = a^{2}\,\text{Var}(X)
Sum of independent X, Y
E(X±Y)=E(X)±E(Y),    Var(X±Y)=Var(X)+Var(Y)E(X \pm Y) = E(X) \pm E(Y),\;\; \text{Var}(X \pm Y) = \text{Var}(X) + \text{Var}(Y)

Discrete distributions

DistributionPMFMeanSD
Geometric (k = trials until 1st success)P(X=k)=(1p)k1pP(X=k)=(1-p)^{k-1}p1p\frac{1}{p}1pp\frac{\sqrt{1-p}}{p}
Binomial(n, p)(nk)pk(1p)nk\binom{n}{k}p^{k}(1-p)^{n-k}npnpnp(1p)\sqrt{np(1-p)}
Poisson(λ)λkeλk!\frac{\lambda^{k}e^{-\lambda}}{k!}λ\lambdaλ\sqrt{\lambda}

Sampling distribution of ȳ (CLT)

Mean
μyˉ=μ\mu_{\bar{y}} = \mu
Standard deviation
σyˉ=σn\sigma_{\bar{y}} = \frac{\sigma}{\sqrt{n}}

For large nn (rule of thumb n30n \geq 30), the sampling distribution of yˉ\bar{y} is approximately Normal regardless of the population shape.

Inference framework

Confidence interval
estimate  ±  (critical value)×SE(estimate)\text{estimate} \;\pm\; (\text{critical value}) \times SE(\text{estimate})
Test statistic
TS=estimatenull valueSE(estimate)\text{TS} = \frac{\text{estimate} - \text{null value}}{SE(\text{estimate})}

Standard error (SE) reference table

ParameterStandard error
Proportion ppSE(p^)=p^(1p^)nSE(\hat{p}) = \sqrt{\frac{\hat{p}(1 - \hat{p})}{n}}
Mean μ\muSE(yˉ)=snSE(\bar{y}) = \frac{s}{\sqrt{n}}
Difference of means μ1μ2\mu_1 - \mu_2SE(yˉ1yˉ2)=s12n1+s22n2SE(\bar{y}_1 - \bar{y}_2) = \sqrt{\frac{s_1^{2}}{n_1} + \frac{s_2^{2}}{n_2}}
Paired mean μd\mu_dSE(dˉ)=sdnSE(\bar{d}) = \frac{s_d}{\sqrt{n}}
Regression slope b₁SE(b1)=sesxn1SE(b_1) = \frac{s_e}{s_x\sqrt{n - 1}}
Mean response ŷ at x₀SE(y^)=se1n+(x0xˉ)2(n1)sx2SE(\hat{y}) = s_e\sqrt{\frac{1}{n} + \frac{(x_0 - \bar{x})^{2}}{(n-1)s_x^{2}}}
Prediction at x₀SEpred=se1+1n+(x0xˉ)2(n1)sx2SE_{\text{pred}} = s_e\sqrt{1 + \frac{1}{n} + \frac{(x_0 - \bar{x})^{2}}{(n-1)s_x^{2}}}

Pooled estimates

Pooled proportion
p^pool=x1+x2n1+n2\hat{p}_{\text{pool}} = \frac{x_1 + x_2}{n_1 + n_2}
SE — pooled (two proportions)
SE=p^pool(1p^pool)(1n1+1n2)SE = \sqrt{\hat{p}_{\text{pool}}(1 - \hat{p}_{\text{pool}})\left(\frac{1}{n_1} + \frac{1}{n_2}\right)}
Pooled variance (two means)
sp2=(n11)s12+(n21)s22n1+n22s_p^{2} = \frac{(n_1 - 1)s_1^{2} + (n_2 - 1)s_2^{2}}{n_1 + n_2 - 2}
SE — pooled (two means)
SE(yˉ1yˉ2)=sp1n1+1n2SE(\bar{y}_1 - \bar{y}_2) = s_p\sqrt{\frac{1}{n_1} + \frac{1}{n_2}}

Chi-square statistic

Chi-square
χ2=(OE)2E\chi^{2} = \sum \frac{(O - E)^{2}}{E}

OO = observed count, EE = expected count. Degrees of freedom depend on the test (e.g. (r1)(c1)(r-1)(c-1) for an r×cr \times c contingency table).

Table Z — Standard Normal CDF, P(Z ≤ z)

Find row for the integer + 1st decimal of z, column for the 2nd decimal. Two halves shown: negative z (left) and non-negative z (right). Range covered: −3.9 to +3.9.

Negative z values: −3.9 to −0.0
z.00.01.02.03.04.05.06.07.08.09
-3.90.000050.000050.000040.000040.000040.000040.000040.000040.000030.00003
-3.80.000070.000070.000070.000060.000060.000060.000060.000050.000050.00005
-3.70.000110.000100.000100.000100.000090.000090.000080.000080.000080.00008
-3.60.000160.000150.000150.000140.000140.000130.000130.000120.000120.00011
-3.50.000230.000220.000220.000210.000200.000190.000190.000180.000170.00017
-3.40.000300.000300.000300.000300.000300.000300.000300.000300.000300.00020
-3.30.000500.000500.000500.000400.000400.000400.000400.000400.000400.00030
-3.20.000700.000700.000600.000600.000600.000600.000600.000500.000500.00050
-3.10.00100.000900.000900.000900.000800.000800.000800.000800.000700.00070
-3.00.00130.00130.00130.00120.00120.00110.00110.00110.00100.0010
-2.90.00190.00180.00180.00170.00160.00160.00150.00150.00140.0014
-2.80.00260.00250.00240.00230.00230.00220.00210.00210.00200.0019
-2.70.00350.00340.00330.00320.00310.00300.00290.00280.00270.0026
-2.60.00470.00450.00440.00430.00410.00400.00390.00380.00370.0036
-2.50.00620.00600.00590.00570.00550.00540.00520.00510.00490.0048
-2.40.00820.00800.00780.00750.00730.00710.00690.00680.00660.0064
-2.30.01070.01040.01020.00990.00960.00940.00910.00890.00870.0084
-2.20.01390.01360.01320.01290.01250.01220.01190.01160.01130.0110
-2.10.01790.01740.01700.01660.01620.01580.01540.01500.01460.0143
-2.00.02280.02220.02170.02120.02070.02020.01970.01920.01880.0183
-1.90.02870.02810.02740.02680.02620.02560.02500.02440.02390.0233
-1.80.03590.03510.03440.03360.03290.03220.03140.03070.03010.0294
-1.70.04460.04360.04270.04180.04090.04010.03920.03840.03750.0367
-1.60.05480.05370.05260.05160.05050.04950.04850.04750.04650.0455
-1.50.06680.06550.06430.06300.06180.06060.05940.05820.05710.0559
-1.40.08080.07930.07780.07640.07490.07350.07210.07080.06940.0681
-1.30.09680.09510.09340.09180.09010.08850.08690.08530.08380.0823
-1.20.11510.11310.11120.10930.10750.10560.10380.10200.10030.0985
-1.10.13570.13350.13140.12920.12710.12510.12300.12100.11900.1170
-1.00.15870.15620.15390.15150.14920.14690.14460.14230.14010.1379
-0.90.18410.18140.17880.17620.17360.17110.16850.16600.16350.1611
-0.80.21190.20900.20610.20330.20050.19770.19490.19220.18940.1867
-0.70.24200.23890.23580.23270.22960.22660.22360.22060.21770.2148
-0.60.27430.27090.26760.26430.26110.25780.25460.25140.24830.2451
-0.50.30850.30500.30150.29810.29460.29120.28770.28430.28100.2776
-0.40.34460.34090.33720.33360.33000.32640.32280.31920.31560.3121
-0.30.38210.37830.37450.37070.36690.36320.35940.35570.35200.3483
-0.20.42070.41680.41290.40900.40520.40130.39740.39360.38970.3859
-0.10.46020.45620.45220.44830.44430.44040.43640.43250.42860.4247
0.00.50000.49600.49200.48800.48400.48010.47610.47210.46810.4641
Non-negative z values: 0.0 to 3.9
z.00.01.02.03.04.05.06.07.08.09
0.00.50000.50400.50800.51200.51600.51990.52390.52790.53190.5359
0.10.53980.54380.54780.55170.55570.55960.56360.56750.57140.5753
0.20.57930.58320.58710.59100.59480.59870.60260.60640.61030.6141
0.30.61790.62170.62550.62930.63310.63680.64060.64430.64800.6517
0.40.65540.65910.66280.66640.67000.67360.67720.68080.68440.6879
0.50.69150.69500.69850.70190.70540.70880.71230.71570.71900.7224
0.60.72570.72910.73240.73570.73890.74220.74540.74860.75170.7549
0.70.75800.76110.76420.76730.77040.77340.77640.77940.78230.7852
0.80.78810.79100.79390.79670.79950.80230.80510.80780.81060.8133
0.90.81590.81860.82120.82380.82640.82890.83150.83400.83650.8389
1.00.84130.84380.84610.84850.85080.85310.85540.85770.85990.8621
1.10.86430.86650.86860.87080.87290.87490.87700.87900.88100.8830
1.20.88490.88690.88880.89070.89250.89440.89620.89800.89970.9015
1.30.90320.90490.90660.90820.90990.91150.91310.91470.91620.9177
1.40.91920.92070.92220.92360.92510.92650.92790.92920.93060.9319
1.50.93320.93450.93570.93700.93820.93940.94060.94180.94290.9441
1.60.94520.94630.94740.94840.94950.95050.95150.95250.95350.9545
1.70.95540.95640.95730.95820.95910.95990.96080.96160.96250.9633
1.80.96410.96490.96560.96640.96710.96780.96860.96930.96990.9706
1.90.97130.97190.97260.97320.97380.97440.97500.97560.97610.9767
2.00.97720.97780.97830.97880.97930.97980.98030.98080.98120.9817
2.10.98210.98260.98300.98340.98380.98420.98460.98500.98540.9857
2.20.98610.98640.98680.98710.98750.98780.98810.98840.98870.9890
2.30.98930.98960.98980.99010.99040.99060.99090.99110.99130.9916
2.40.99180.99200.99220.99250.99270.99290.99310.99320.99340.9936
2.50.99380.99400.99410.99430.99450.99460.99480.99490.99510.9952
2.60.99530.99550.99560.99570.99590.99600.99610.99620.99630.9964
2.70.99650.99660.99670.99680.99690.99700.99710.99720.99730.9974
2.80.99740.99750.99760.99770.99770.99780.99790.99790.99800.9981
2.90.99810.99820.99820.99830.99840.99840.99850.99850.99860.9986
3.00.99870.99870.99870.99880.99880.99890.99890.99890.99900.9990
3.10.99900.99910.99910.99910.99920.99920.99920.99920.99930.9993
3.20.99930.99930.99940.99940.99940.99940.99940.99950.99950.9995
3.30.99950.99950.99950.99960.99960.99960.99960.99960.99960.9997
3.40.99970.99970.99970.99970.99970.99970.99970.99970.99970.9998
3.50.99980.99980.99980.99980.99980.99980.99980.99980.99980.9998
3.60.99980.99990.99990.99990.99990.99990.99990.99990.99990.9999
3.70.99990.99990.99990.99990.99990.99990.99990.99990.99990.9999
3.80.99990.99990.99990.99990.99990.99990.99991.00001.00001.0000
3.91.00001.00001.00001.00001.00001.00001.00001.00001.00001.0000

Table T — t-distribution critical values

Two-sided critical values tt^{*} for the most common confidence levels (80%, 90%, 95%, 98%, 99%). For a one-sided test at level α\alpha, use the column whose two-sided level equals 12α1 - 2\alpha. The last row (\infty) matches z critical values.

Table T — two-sided critical values t* by confidence level and df
df80%90%95%98%99%
13.0786.31412.70631.82163.657
21.8862.9204.3036.9659.925
31.6382.3533.1824.5415.841
41.5332.1322.7763.7474.604
51.4762.0152.5713.3654.032
61.4401.9432.4473.1433.707
71.4151.8952.3652.9983.499
81.3971.8602.3062.8963.355
91.3831.8332.2622.8213.250
101.3721.8122.2282.7643.169
111.3631.7962.2012.7183.106
121.3561.7822.1792.6813.055
131.3501.7712.1602.6503.012
141.3451.7612.1452.6242.977
151.3411.7532.1312.6022.947
161.3371.7462.1202.5832.921
171.3331.7402.1102.5672.898
181.3301.7342.1012.5522.878
191.3281.7292.0932.5392.861
201.3251.7252.0862.5282.845
211.3231.7212.0802.5182.831
221.3211.7172.0742.5082.819
231.3191.7142.0692.5002.807
241.3181.7112.0642.4922.797
251.3161.7082.0602.4852.787
261.3151.7062.0562.4792.779
271.3141.7032.0522.4732.771
281.3131.7012.0482.4672.763
291.3111.6992.0452.4622.756
301.3101.6972.0422.4572.750
351.3061.6902.0302.4382.724
401.3031.6842.0212.4232.704
451.3011.6792.0142.4122.690
501.2991.6762.0092.4032.678
601.2961.6712.0002.3902.660
801.2921.6641.9902.3742.639
1001.2901.6601.9842.3642.626
2001.2861.6531.9722.3452.601
5001.2831.6481.9652.3342.586
∞ (z)1.2821.6451.9602.3262.576

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